Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU

Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU

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Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU

Sebastian Salas-Vega, Adria Haimann & Elias Mossialos

To cite this article: Sebastian Salas-Vega, Adria Haimann & Elias Mossialos (2015) Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU, Health Systems & Reform, 1:4, 285-300, DOI: 10.1080/23288604.2015.1091538

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Research Article

Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU

Sebastian Salas-Vega*, Adria Haimann and Elias Mossialos London School of Economics and Political Science; London, UK



Defining Big Data


Data Sources and Search Strategy


Ongoing Initiatives

Challenges Surrounding Use of Big Data in Health Care


A Balanced Framework Encompassing Data Confidentiality

and Use

Integration of Data Systems

Ensuring Quality of Data Collection, Analysis, and Oversight

Untangling Hype from Reality



Abstract—As global policy makers prioritize big data policy, it is

important to try to outline expected outcomes vis-�a-vis health sector

objectives. We identify initiatives aimed at promoting the use of big

data in European Union (EU) health care, highlight expected

challenges, and use these to evaluate EU big data policy

developments to the extent that they are able to advance health

sector priorities. A comprehensive approach is used to capture and

examine peer-reviewed and gray literature publications on the use

of big data in global health systems. This approach involved

electronic database and specialist website searching, as well as

complementary use of search engines and qualitative inputs from

key EU policy stakeholders. Ongoing health data initiatives revolve

around data center development, confidentiality and security, e-

health and m-health, and genomics and bioinformatics. The

literature acknowledges several main challenges to the successful

integration of big data in health care, classified as either ethical

(confidentiality and data security, access to information) or

technical (data reliability, interoperability, data management and

governance). EU data policy has started to address these issues,

though additional work remains. A larger outstanding challenge is

the lack of a comprehensive health and research policy strategy for

big data that targets sectoral objectives. It remains unclear how big

data integration will affect the quality and performance of health

care in the EU. The promises of big data are being eroded by a

failure to develop a coherent approach to adequately address

conceptual, ethical, and technical challenges pertaining to its use

within EU health systems.


“Big data” has become a popular theme in European Union

(EU) policymaking. The European Commission (EC)

recently inaugurated its “Digital Agenda for Europe” (DAE)

as one of the seven pillars of the Europe 2020 initiative1 to

capitalize on the data revolution and foster innovation and

economic growth throughout the Union. Calls to expedite the

Keywords: big data, Europe, health policy, qualitative research, reform

Received 19 March 2015; revised 1 September 2015; accepted 3 September 2015.

*Correspondence to: Sebastian Salas-Vega; Email:


Health Systems & Reform, 1(4):285–300, 2015 Copyright� Taylor and Francis Group, LLC ISSN: 2328-8604 print / 2328-8620 online DOI: 10.1080/23288604.2015.1091538

creation of a single market for big data have since emerged

from recent European Council Summits (October 2013), and

the EC has responded by adopting legal, technical, and logis-

tical aims for big data reforms in the EU.2-4 Tangible out-

comes from the EC’s pivot to data policy reform include the

publication of guidelines on open standard licenses, data

sets, and reuse charging; as well as revisions to the Public

Sector Information directive, making public data “open by

default”5 and growing the ecosystem for big data.6,7 Perhaps

the most visible outcome of big data policy reform was the

signing of a contractual public–private partnership (cPPP)

between the EC and industry (Big Data Value Association)

on 13 October 2014, which has pledged €2.5 billion to

“strengthen the data sector in Europe.”8

Efforts by European policy makers to grow data availabil-

ity, processing, and use in the EU appear to be driven by

socioeconomic aspirations. Union publications repeatedly

highlight expectations of data-driven social and economic

gains for Europe,2-5,8-10 yet provide limited detail on how

they are to materialize.

The likelihood of data policy reforms engendering social

and economic gains can arguably be estimated by referring

to legislative goals and agendas. In health, key EU policy

objectives include (Table 1) strengthening of health system

effectiveness, accessibility, resilience, quality, and perfor-

mance,11 as well as the promotion of health research.12 The

extent to which data policy reforms advance these objectives

in health provides a measure of the marginal benefit from

ongoing political and financial investments in EU data.

Importantly, however, major conceptual dilemmas in suc-

cessfully bridging big data policy and EU health care remain:

first, there is no framework for measuring data-driven progress

across EU member states toward common health policy and

research objectives. Second, data policy developments have not

been informed by conceptual issues that underlie sectoral objec-

tives, such as how to best measure health system quality and per-

formance. Third, health data collection processes across

countries are not systematized, and comprehensive structures

capable of incentivizing common progress remain undefined.

Finally, the extent to which obstacles to integrated big data–

health policymaking are compounded by poor coordination

within EU political institutions remains unclear.

As a consequence, emerging EU initiatives on big data5,13,14

have not been brought under a coherent conceptual framework

that captures, coordinates, and advances health policy and

research objectives. Recent communications on the push for

data-driven economies have even acknowledged that sectoral

priorities for data research and innovation remain unidentified,2

even as data policy implementation continues. Set beside

health system improvement initiatives that presuppose avail-

ability of data, as well as communication across performance

assessment and publication agendas, it is surprising that robust

EU policies cutting across data and health remain lacking.

EU health policy stands at a critical juncture: greater

action must be taken to help carefully weave the fabric of

data-laden health care across the EU. Failure to do so risks

(1) development of crude data programs that are incapable of

adequately addressing the needs of health systems and (2)

EU Health Sector Objectives Scope of Interest to EU Policy Makers

� Effectiveness A key component of health care quality and health system performance, effectiveness is defined as health systems’ ability to produce positive health outcomes across the population. Data on comparative

effectiveness of health systems can be informed by EU-wide indicators.

� Accessibility A fundamental tenet of European health care that is characterized by ease of access to medical treatment. EU states are obligated to administer socially inclusive health systems.

� Resilience Health systems must be resilient to both fiscal and nonfiscal challenges; that is, they must be able to quickly adapt to unexpected challenges in supply and demand. The EC is tasked with supporting member states

in this work through analysis, forecasts, and reform recommendations.

� Research and innovation EU research and data analysis is prioritized to help tackle health afflictions and societal challenges and promote learning. Some research is of such scale and complexity that it requires EU level

coordination to improve efficiency in use of resources to address common challenges, such

as improving health.

TABLE 1. Key EU Health Sector Objectives and Scope of Interest to EU Policy Makers. Source: Adapted from the European


286 Health Systems & Reform, Vol. 1 (2015), No. 4

continued fragmentation in EU data strategies,3 driven by a

lack of common vision for reform.

To maximize the benefit of further data policy reforms to

the health sector, policy must be able to exploit synergies in

the merger between health and data. A critical examination

of the interaction between both fields is therefore needed. To

address this issue, this article takes a comprehensive

approach to capture ongoing EU initiatives and challenges

regarding data use in health. These are evaluated alongside

EU policy developments to provide a realistic assessment of

the scope for data-driven improvements to health.

We find that EU policy makers have begun to tackle tech-

nical and ethical issues regarding data use, though with

noticeably less attention to focused applications in health.

Policy developments have also yet to address many of the

conceptual challenges that underlie attainment of health sec-

tor ambitions. In light of these gaps, we conclude that the EU

should reevaluate its strategy for data policy development to

help meet sectoral objectives and maximize the value drawn

from its investments.


Existing Definitions

This article does not set out to review definitions of big data,

as these are already available elsewhere.15-17 Suffice it to say

that no single definition of big data is universally accepted,18

though certain definitions do stand out. Big data, for instance,

is most commonly defined by the 3 Vs (volume, velocity, and

variety),19-23 as well as other variants: there are the “4 Vs,”

which captures data volume, velocity, variety, and verac-

ity24,25 and the “5 Vs,” which also considers value.26,27 Big

data can otherwise be defined as a large collection of com-

plex data sets,28-32 characterized by the existence of struc-

tured and unstructured variable sets.33,34

EU Definitions

Nonsystematic use of big data definitions also extends to

the EU policy arena. The EC describes big data as data

that is “difficult to process with current data management

tools and methods.”2 The same source, however, also

refers to big data in terms of the 3 Vs: “Large amounts

of different types of data produced with high velocity

from a high number of various types of sources.”2 Defin-

ing big data nonsystematically undermines the establish-

ment of normative standards to apply in regulation and

may also give rise to internal inconsistencies—one EC

definition identifies big data through its development, the

second through its processing.

In the absence of an a priori reason to do otherwise,

we define big data through a merged EC definition: a

large amount of different types of data produced with

high velocity from various types of sources and which

must be processed through novel approaches to bypass

processing limitations extending from current manage-

ment tools and methods.


A comprehensive approach—consisting of electronic data-

base and specialist website searching, as well as complemen-

tary use of search engines and qualitative inputs from key

policy stakeholders—was used to capture the peer-reviewed

and non-peer-reviewed literature commenting on big data

use in health care.35 From this, we critically summarize

active data initiatives and identify overarching principles and

challenges associated with their merger to health. These are

set beside a discussion of ongoing big data policy reforms in

the EU to shed light on the extent to which they can realisti-

cally promote European health objectives. Appropriate strat-

egies for coordinated data–health development are then

identified and discussed.


A detailed search strategy was used in July 2014 to massively

expand peer-reviewed publications indexed through Medline

via PubMed, Scopus, Google Scholar, and EconLit. The

search strategy included logic string combinations of relevant

text words, keywords, and medical subject headings, devel-

oped through internal consensus among researchers and fur-

ther refined with input from research librarians (Table 2). A

hierarchical procedure for database searching was used to

independently capture the literature discussing big data in

health care (first search level) and that discussing policies

pertaining to big data in health care (second search level).

Only relevant hits published in English since 2005 were


Two assessors independently conducted title and abstract

screening of all captured articles to identify those that

focused on big data in US or EU health care. Where there

was disagreement on inclusion eligibility (n = 12), consensus

was reached through discussion. Papers discussing technical

big data computing techniques, detailed methods for big data

statistical analysis, and cost analyses of big data integration

were beyond the scope of this article and were thus excluded.


Salas-Vega et al.: Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU 287

All papers meeting selection criteria were included in the

review (n = 164).

The search strategy was also extended to capture the gray

literature. Full and modified search strategies were applied

within relevant organization websites (e.g., EC, Eurostat,

European Parliament) and search engines (Google). Litera-

ture searches were also complemented by publication recom-

mendations and qualitative inputs from two content experts

and key policy stakeholders at Eurostat (EC). By capturing

peer-reviewed and non-peer-reviewed publications, this arti-

cle incorporates viewpoints on the use of big data in health

care from academia, government, and industry.

This approach is used to combine different perspectives

on the technical, ethical, and conceptual challenges that per-

tain to the use of big data within and across health systems.

We preface this synthesis with a discussion of ongoing big

data initiatives in the EU to help assess the adequacy of

recent big data developments vis-�a-vis health sector needs

and characteristics.


This section is divided into two components: first, ongoing

big data initiatives in health across the EU are reviewed, at

both national and European levels. To the extent that EU pol-

icymaking exists to manage and coordinate national policy

efforts, prominent challenges associated with additional

development of big health data arrangements are then identi-

fied and discussed within the context of EU policy. We con-

clude that despite the proliferation of policy targeting big

data use in the EU, significant challenges remain to its suc-

cessful application in EU health care.


Big data in health is a broad theme that can cover a wide array

of topics. A discussion of ongoing initiatives across the EU is

therefore developed on the basis of a four-tiered thematic

framework encompassing topics debated in influential US and

EU big data publications: data centers,24,36 confidentiality

and data security,24,25,37 e-health and m-health,24,25,37 and

genomics and bioinformatics.24

Data Centers

Countries are vigorously attempting to develop data centers—

databases that can store vast amounts of diverse health informa-

tion—for the purpose of clinical practice and research,7,18,38-52

public health surveillance,53-57 medical training and learning,58-

62 and pharmaceutical development and marketing.63-71

The literature suggests that research in oncology, cardiol-

ogy, neurology, mental, and population health is benefiting

from the growth of big data,38,39,50,51 with the discussion on

cancer predominating.40-48 The quantity of generated cancer

data is rapidly increasing in the EU and the United States,

particularly through tumor genome sequencing, computed

tomography and magnetic resonance imaging, test results,

and medical history.42 In some instances, these data are being

compiled into cancer registries to give insights into timing of

diagnosis, as well as long-term patient outcomes and treat-

ment effects.40 In Italy, IBM has partnered with an Italian

cancer institute to leverage available data with the aim of

improving cancer care.45 In the UK, Oxford University has

established its Big Data Institute and the Chan Soon-Shiong

Oxford Center for Molecular Medicine to collect and analyze

large anonymized medical datasets and promote data-driven

String Terms

String 1: big data (search level: 1st + 2nd) PM: (big data [tiab]) OR (big data [tw])

GS/EL: (“big data”)

S: (“big data” [title/abs/keyw])


String 2: health care (search level: 1st + 2nd) PM: (healthcare [tiab]) OR (health care) OR (health) OR (health care system)

OR (delivery of health care [mh])

GS: (health [title]) OR (healthcare [title]) OR (“health care” [title]) OR

(“health care system” [title])

S: (healthcare [title/abs/keyw]) OR (health [title/abs/keyw]) OR

(“health care system” [title/abs/keyw])


String 3: policy (search level: 2nd) PM: (polic* [tiab]) OR (legislation [tiab]) OR (legislation [mh])

OR (legislat* [tiab])

GS: (policy) OR (policies) OR (legislation) OR (legislative)

S: (polic*[title/abs/keyw]) OR (legislation [title/abs/keyw]) OR

(legislat* [title/abs/keyw])

TABLE 2. Literature Review Search Terms (Search Levels). PM, PubMed; GS, Google Scholar; EL, EconLit, S, Scopus

288 Health Systems & Reform, Vol. 1 (2015), No. 4

personalization in cancer medicine.49,51 Also in the UK, Pub-

lic Health England and the National Cancer Intelligence Net-

work have moved to build the world’s largest database of

cancer patients to coordinate and develop analysis and intel-

ligence and improve oncology prevention, treatment, and

outcomes.48 At a European level, the recently launched Inno-

vative Medicines Initiative 2 has issued calls to fund the

development of knowledge repositories to enhance personali-

zation of patient care, and future topics seek to promote the

development of value-based health data systems in the EU.52

While clinical registries and longitudinal patient health sur-

veys are increasingly being consolidated, the extent to which

these data sets are being utilized to effectively address health

needs remains less clear.

Big data research centers are also being used for public

health surveillance. Researchers and health officials are using

aggregated individual data to monitor global disease trends in

real time.53-56 Visual analytics are also being used to help

choose appropriate intervention policies on the basis of food-

borne illness trends.57

Big data centers are also being integrated into medical

training and educational initiatives.58-62 Outside of the EU,

health professional education is using longitudinal data

related to curriculum structure to determine appropriate com-

petencies and course trajectories.59

Additionally, pharmaceutical data pools are being lever-

aged for pharmacovigilance,65-67,71 monitoring of develop-

mental life cycles,63,64 and pharmaceutical marketing and

commercialization.69 In Europe, big data has been used to

help with pharmaceutical development through the Innova-

tive Medicines Initiative, an EU-funded public–private pro-

gram intending to pool data and accelerate medicinal


There are indications that the pharmaceutical industry

stands to gain from greater integration of health data,72 yet

opposing claims also exist. There is the view that increased

data access will facilitate drug safety and efficacy assess-

ment,73 yet others have suggested that added data rigors—for

example, the establishment of health information protection

systems—may disadvantage industry by increasing legal,

reputational, and financial risks.74

Confidentiality and Data Security

Strategies for confidentiality and data security are key focus

areas in the big data literature. In terms of policy, a number

of EC communications2,75 and directives5,76 have begun to

shape a common legal framework targeting patient confi-

dentiality concerns by establishing effective data protection

and network and information security rules. More recently,

the EC adopted a proposal for a general EU framework for

data protection in January 2012 with a view to modernize

and harmonize data protection rules.77 The European Data

Protection Directive complements this by prohibiting data

processing that may infringe fundamental freedoms or pri-

vacy, except if data subjects give their explicit consent or if

circumstances permit derogation.76 The EC has also recom-

mended that information and communication technologies

integrate the principle of privacy-by-design and default and

make use of privacy-enhancing technologies.77,78 The extent

to which these nonbinding guidelines have been adopted nev-

ertheless remains unclear.

At an operational level, US health care industries are cur-

rently trying to tackle privacy challenges through the crea-

tion of secure data clouds22,79-82 that make use of privacy

mechanisms such as obfuscation80,83,84 to safeguard health

confidentiality. Privacy-preserving record linkage and ana-

lytics—in which algorithms run on encrypted data—are also

being developed to decouple personal and sensitive informa-

tion (e.g., cancer status), helping to maintain patient anonym-

ity and thus enabling its protected use in research.31,85,86

Because digital security is a globally relevant issue with rep-

licable, adaptable, and interdependent solutions,87,88 the EU

likely benefits from global developments in big data security.

Big data in health often involves the aggregation of

patient-level information for secondary use. To address pub-

lic concerns regarding confidentiality and misuse of collected

personal data89 that underpin legal and ethical frameworks

for its use, informational campaigns may be used to raise

awareness of data privacy rights and protection mechanisms.

The EC has stated that it intends to use educational cam-

paigns to inform the public on ways to reduce confidentiality

and data security risks,2 though operational details remain

lacking. National authorities appear to be taking a more pro-

active role in this regard: the English National Health Ser-

vice, for instance, recently led a public campaign to inform

citizens about, a program making wider use of dei-

dentified health information for research purposes,90 though

indications suggest that it failed.91 It is unclear how the EU

and member states have moved to effectively coordinate

informational campaigns.

e-Health and m-Health

Several articles emphasize that big data is expected to

improve disease management by better informing individual-

ized diagnosis and treatment.29,92-95 One example of a per-

sonalized medicine initiative is a Danish theoretical service

model called Co-production of Health, designed to unite

health care and self-care to provide “health added value” by

Salas-Vega et al.: Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU 289

computing through personalized models that are context-

aware.96 This model for health care targeting, however,

remains unimplemented and unvalidated. Ongoing efforts to

create pan-European biobanks—for example, the Central

Research Infrastructure for Molecular Pathology and the

Organisation for Economic Co-operation and Development’s

Global Biological Resources Centers Network—also offer an

opportunity to individualize care by helping to reveal the dis-

ease relevance of genes.94 For its part, the EU has moved to

fund academic and industry initiatives in personalized medi-

cine, such as PerMed and EuroBioForum, that promote data

sharing.77 Despite these initiatives, however, the full poten-

tial of big data in personalized medicine is unlikely to be met

without parallel advances in regulatory, reimbursement, and

privacy legislation.97,98

Emerging mobile and computer-based health applications

have helped patients share personal treatment experiences

and promote physical and mental health.99-102 These applica-

tions are often combined with sensor systems that collect vast

amounts of information and serve a variety of purposes, rang-

ing from elderly assistance to informing overweight patients

about increased health risks.32,103-105 From this perspective,

complex algorithms, artificial intelligence, and machine

learning are needed for dynamic, secondary analysis of rap-

idly growing health data sets that increasingly link patient-

generated data,106-108 yet these issues are not reflected in EU

policy. Technologies supporting real-time data collection and

processing may be particularly useful in enabling the EU to

effectively and quickly adapt to changing health environ-

ments.11 Greater legislative clarity is required to effectively

coordinate data-generating applications in health.77

Electronic health records (EHRs) similarly provide an

abundance of data with potential value to clinical medi-

cine.33,47,109-113 Many EU countries, including The Nether-

lands, Denmark, and the UK, are introducing EHR systems

that update individual health history following medical con-

sultations or treatment.114 These countries, have been

advised by Eurostat (EC) to take the lead in investigating

uses of such systems for statistical and big data analytical

purposes.114 Though this coincides with the EC’s decision to

support “lighthouse” data initiatives across the EU, it may

prove challenging for the bloc to successfully integrate

national EHR data to the benefit of health policy objectives

regarding cross-country comparisons115 without international

management and coordination.

Genomics and Bioinformatics

Genomics and bioinformatics is another key topic cross-link-

ing big data and health. Two main uses of genomics include

the sequencing of malignant tumors and genomes.116 The

amount of captured genetic data is rapidly growing as a result

of next-generation technologies that use high-throughput

DNA sequencing117-119 to boost genetic profiling capacity.

Sequencing and translational bioinformatics24,120-123 rep-

resent big data applications that require massive amounts of

storage and analytical power for data processing. To accom-

modate, infrastructure and big data tools, including cloud

computing and storage techniques, are being tailored for use,

particularly in the genetic and genomic sciences.116,120,124-

130 An example of this in the EU is the Helix Nebula Project.

This public–private initiative across information technology

providers, the European Organization for Nuclear Research,

the European Molecular Biology Lab, and the European

Space Agency, uses cloud services to perform on-demand,

large-scale genomic analysis.131 In the UK, public–private

partnerships are also investing in big data health research

centers that focus on studying the early stages of disease

using genomic and chemical screens.51



Several key challenges are frequently said to present an

obstacle to big data use in health care: confidentiality and

data security,24,89 access to information,132,133 data reliabil-

ity,34,134 interoperability,135,136 and management and gover-

nance.137,138 This section discusses these challenges within

the context of EU big data policy and initiatives.

Confidentiality and Data Security

Patients fear that misappropriation of their health informa-

tion—particularly genetic data34,139,140—may adversely

affect personal circumstances, including insurance coverage

and employment.25 Unfortunately, data access and confi-

dentiality risks are directly correlated.141 In excluding scien-

tific and medical data from general principles making public

data open-by-default, the EC’s Open Data policy appears to

reflect these concerns.5 The organization has instead opted

for complementary legislation to address unique confidential-

ity challenges in health142 and cross-border care delivery.143

Given that public approval is a chief regulator of the political

will for reform, many of the remaining data policy challenges

identified in this article may flow from this central point.

The EC has planned to further address patient confidenti-

ality concerns through amendments to existing data protec-

tion directives,76,89,144 following EU constitutional revisions

that strengthen personal data protection rights (Treaty of Lis-

bon). These legislative changes are to unify EU initiatives on

290 Health Systems & Reform, Vol. 1 (2015), No. 4

confidentiality and data security and to provide a more flexi-

ble legal framework that can rapidly adapt to changing tech-

nologies. However, data protection reforms have arguably

been few and limited to enhancing transparency89 and confi-

dentiality in lawful data processing. In the context of sensi-

tive health data, it is unclear whether this promotes broader

ambitions for data-driven health sector development, innova-

tion, and private sector involvement.

Access to Information

A competing challenge is access to information. Individual

perceptions of powerlessness in data control are currently at

odds with organizational beliefs of data ownership.145 Con-

cerns regarding data access and use legitimize the question:

are society’s best interests in mind as data access pathways

are negotiated? For the consumer, a primary concern is third

party access and data control.139,140 Companies, too, are

interested in internal collection and use of information63 but

also worry about disclosure of intellectual property.146

The financial and nonfinancial interests of health care pro-

viders may also be challenged by information sharing. These

organizations may consequently be less inclined to disclose

information regarding performance132 or may actively work to

game data return through exception reporting147,148 or cherry-

picking of patients,149 giving rise to concerns of data validity.

Across the EU, several countries have made an effort to

provide more information to patients on health-related issues.

The European Collaboration for Health Optimization proj-

ect—a Spanish initiative to collect health data and analyze

variations in European medical practice and health out-

comes—and the English program serve as two


To expand data access, the EC has also adopted an open-

by-default principle to public sector information, freely mak-

ing it available for commercial and noncommercial reuse.5

Though open data is not necessarily “big,” it can be used to

help accelerate the maturation of a big data ecosystem.6,7 The

amended Public Sector Information directive nevertheless

excludes scientific and health-related information, diminishing

its applicability to health data. Elsewhere, EU government

agencies, academia, industry, nongovernmental organizations,

and international organizations have moved to enhance clinical

data sharing in an effort to enhance trial transparency and ana-

lytical reproducibility.152-156

Data Reliability

Data reliability is another often-cited challenge to implemen-

tation and use of big data systems in health.34,113,134

At an operational level, manually fed electronic health

data may be prone to error and bias from human entry. Yet,

regularized systems can also introduce systematic bias into

data collection and analysis.28,34,133,134 For example, under-

funded organizations lacking adequate technological infra-

structures to document and share information may only be

able to capture data from a subset of the population34 or may

in fact capture incorrect information if collection or process-

ing algorithms are flawed.113 As others have pointed out, if

an EHR lacks information about a medical event, it is not

necessarily because the event did not occur.135 Blind accep-

tance of big data should therefore be cautioned against: there

is a need for measured use of big data and careful interpreta-

tion of results, as well as investment in big data system

development and audit.

EU member states are actively trying to improve data reli-

ability. The UK Hospital Episode Statistics (HES), for

instance, is regarded as one of the world’s most comprehen-

sive health data sets, processing over 125 million patient

records per year for all admissions (1990–), outpatient

appointments (2003–), and accident and emergency attend-

ances (2008–) occurring within English public hospitals.

Despite its tremendous value to research and clinical prac-

tice, HES has historically been criticized for unreliable sec-

ondary diagnoses.157 In response, UK health authorities

refocused efforts to improve clinical coding in 2001157 and

established an assurance program for hospital data in

2007.158 Recent studies have reported improvements in HES

data quality,159 demonstrating the value of regulatory over-

sight in ensuring health data reliability. Despite EU health

policy and research objectives that require a healthy source

of data,115 data quality initiatives remain lacking at an EU



Data interoperability is another major challenge to further

development of medical data systems.132-135,160 Interopera-

bility is crucial for recording health information, developing

common interfaces, agreeing on common data sets, and

defining quality standards.114 Interoperability necessitates

development of data platforms in an international, compara-

ble context and thus requires common principles. Kenny

Simmen, Vice President of Janssen’s Infection Diseases,

Research and Early Development, alluded to this point by

explaining that different countries have different regulations,

making it difficult for coordination and collaboration

between researchers.161 Interoperability of EU data sets is

further complicated by varying clinical standards and


Salas-Vega et al.: Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU 291

The EC’s Digital Agenda for Europe highlights an oppor-

tunity to deliver sustainable economic and social benefits

from a digital single market that is based on interoperable

applications.4 The EU recently sponsored the epSOS project

to outline how member states can integrate e-health architec-

tures and established the e-Health Network as the main stra-

tegic and governance body in the EU to work toward

interoperability of cross-border e-health services.77,143 How-

ever, participation in these networks remains voluntary,143

and only this year will the EC—with the endorsement of the

e-Health Network—propose an e-Health Interoperability

Framework to help establish legal, organizational, semantic,

and technical specifications for interoperable cross-border e-

Health services.77 Notably, the e-Health Interoperability

Framework will include a non-exhaustive list of data to be

collected through patient summaries and shared as part of

cross-border data sharing initiatives to promote continuity of

EU care.77 This comes alongside a push by the EC to exam-

ine member states’ laws on electronic health records in order

to assess the legal aspects of interoperability.77 In the

absence of additional progress, it nevertheless remains

unclear how these initiatives will enhance big health data

interoperability across the Union and effectively promote

health policy and research objectives.

Management and Governance

There is little information in the published literature discussing

governance and management of health data at the EU level.

Big data leaders within the EC have indicated to us that DG

Health and Food Safety (DG Sante), DG Communications

Networks, Content and Technology (DG Connect), and DG

Research and Innovation are the main EU governance bodies

capable of informing big data debates in health care. Yet, there

is little evidence of coordinated efforts to promote health sec-

tor objectives through the use of big data. Indeed, although

European authorities are responsible for assembling compara-

ble health-related data across member states and developing

mechanisms for comparative analysis,115 DG Sante and Euro-

stat have only recently begun to outline strategies to improve

comparative health reporting.162

It also remains unclear how responsibilities regarding

health data systems are split across European agencies. For

example, although Eurostat and DG Sante are responsible for

expanding and improving the European Health Survey Sys-

tem,162 it is unclear how other relevant bodies (e.g., DG Con-

nect) support this endeavor. From a user’s perspective,

improved clarity from the EU on health data governance and

management is needed138 and may facilitate access and

effective data use.


Our review of initiatives and challenges regarding big data in

EU health care highlights four key lessons for big data policy

development across the EU. We present these here alongside

an evidence-based discussion of potential strategies for reme-

diation. Though these are framed around EU health data and

policy developments, they also provide globally relevant

insights on how to develop capacity and coherent policy in

digital health resources to promote domestic and interna-

tional health policy initiatives.



As EU policy makers begin to call for greater integration of

health data sets,163,164 it is important to bear in mind that as

in any security domain, the weakest link can break the chain.

Relegating policy responsibilities regarding health confi-

dentiality and data security to individual member states may

create a weak and non-uniform cross-border data privacy

architecture. EU governing councils therefore have an impor-

tant role to play in establishing the framework for common

progress in confidentiality and data security.

Despite ongoing efforts by the EC to update data protec-

tion legislation to accommodate rapidly evolving technolo-

gies,78,89 a more comprehensive and coherent policy on the

fundamental right to personal data protection is

needed.144,165 This should balance confidentiality, data

access and security in health, while also defining standards

for data ownership and control, reuse, cross-border flow,

storage, and processing.77 Existing legal frameworks define

personal data, establish principles for its lawful processing,

and also strengthen individual access rights.144 However,

they may be at times vague, overly restrictive, and internally

inconsistent: for instance, they may call for the removal of

regulatory red tape, but at the same time prohibit the process-

ing of genetic or health data without providing exclusion for

qualified third-party use that nevertheless safeguards per-

sonal data protection.144 Big data systems in health must be

strategically designed to ensure patient confidentiality, while

granting timely access to qualified users in academia and

industry, to fully achieve health policy and research objec-

tives.141 The EC should take this into consideration as it

finalizes its review of the EU legal framework for personal

data protection, which aims to strengthen individuals’ rights

and facilitate commercial data use.78

As data policy reform is debated, it may be useful to con-

sider stakeholder interests regarding data access. Health care

professionals, researchers, and industry all have valid interests

292 Health Systems & Reform, Vol. 1 (2015), No. 4

in comprehensive access to big health data, such as improve-

ment in quality of care, clinical trial development, and phar-

macovigilance. On the other hand, health data access demands

from formal (e.g., insurers) and informal (e.g. hackers) entities

may be unacceptable. The EC should take this into consider-

ation, especially as it fields nongovernmental partnerships to

expedite data growth, processing, and use in the EU.10


Centralized EU governance provides the bloc with an oppor-

tunity to be at the vanguard of multinational health data inte-

gration. Although several initiatives have started to address

e-Health interoperability,77 major outcomes are only

expected this year and their impact on data program remains

unknown. Many related issues—for example, researcher

access to interoperable e-Health data and promotion of health

policy objectives—also remain unresolved.

There is hope that big data integration will optimize pre-

ventative care by helping to address risk factors92,99 and

improve measurement of health system performance.166

Cross-country comparisons of health information is in line

with EU competencies, interests, and health policy and

research objectives.115 However, comparisons should be

based on conceptual linkages between health and data use—

for example, that leverage valid quality indicators—to ensure

progress toward these objectives.

A particularly valuable opportunity for big data integra-

tion within the EU health context may be in the sharing of

clinical evidence.77 By leveraging across established EU

health registries, well-designed and highly powered second-

ary studies may help attenuate traditional sources of bias,167

better inform clinical applications (e.g., comparative effec-

tiveness research), and thus promote member state and stake-

holder interests in health technology assessment.77

Nevertheless, data quality and interoperability must first

be promoted to ensure meaningful comparison. International

data sets often reflect slightly different definitions of health

and health care and may be developed using varying algo-

rithms, making linkage difficult and of questionable quality.

Existing differences in clinical coding and quality measuring

practices,168 for instance, can bias cross-country comparisons

of hospital performance. It also remains difficult to produce

high-quality, error-free linkages across health data sets even

within highly developed health systems.169

With this as a backdrop, there is no EU-level body dedi-

cated to data monitoring and integration efforts in health,

even though cooperative health data collection, analysis,

monitoring, and dissemination activities fall within the legal

remit of European public health interests and strategies.115

The Reform Treaty of 2007 in fact reaffirms the EC’s role in

comparative health policy, and both the EC and European

Parliament have called for greater exchange of information

by assembling comparable health data across member states

and developing mechanisms for comparative analysis,

including through multilevel health indicators.115,170

Although a set of European core health indicators exist on

health status, health determinants, and care across EU mem-

ber countries, they remain less than perfectly comparable,

data are not always available, and it is unclear how they

correspond to recent data policy reforms.171

Finally, industry bears legitimate interest in promoting

data quality improvements and closer integration within and

across national boundaries.172 European policy makers

should take industry as a partner in ongoing big data reform

initiatives, particularly as privately managed health data

sets—often existing beyond the reach of public direc-

tives5,13—gain prominence.173



The EU is unlikely to capitalize on big data in health until

there is high-quality data collection and processing systems

and well-defined governance providing oversight.

Sitting within Europe’s political and legislative hub, EU

policy makers should give greater clarity to big data gover-

nance in health care, particularly as it applies to cross-border

data use. The statistical service of the EU, Eurostat (EC), has

already created a Big Data Task Force to refine use of big

data for European official statistics. This task force, however,

does not focus on data use in health but rather on its applica-

tion to all EU statistics. Given the unique challenges associ-

ated with health data,174 EU policy makers should consider

creating expert teams to oversee EU health data quality


Notably, the EU is currently witnessing the arrival of

affordable personal data-generating devices80,104 that may

complement traditional streams of health data and enhance

provider knowledge.175 Broader policy developments regard-

ing the extension of property rights to personal data61 may,

however, ultimately define collection and use of information

available through this medium.


There is a tremendous amount of hype surrounding big

data in health care. Some anticipate that big data will

help enhance efficiency, quality, and equity in health care

delivery,25 whereas others expect big data to produce

Salas-Vega et al.: Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU 293

tremendous cost savings.82,176,177 The EC frequently

adopts this rationale to justify European investments in


We do not dispute that big data has the potential to

improve health and health care and lead to efficiency savings

by better informing sector processes. However, to realize

these expectations, conceptual, ethical, and technical con-

cerns must first be overcome. Ongoing failures to tailor data

policy to these issues points to a European strategy for big

data use in health care that is not grounded in a coherent

vision for policy development. These are likely to have cre-

ated an unconnected patchwork of effects in health care

across member states,178 and they draw to question whether

widely acknowledged expectations for big data will be met,

at least insofar as EU health policy and research objectives

are concerned.


European policy makers have started to develop policy

affecting the European data market. Policy developments

reflect recognition within the EC of the need for a com-

mon big data strategy and infrastructure. In this article,

we examined the extent of big data policy coordination

with broader EU health policy and research objectives.

Policy developments have started to address technical

(e.g., interoperability) and ethical (e.g., legal frameworks

regarding confidentiality and data security) challenges to

the use of big data in health care, two major barriers to

e-Health development identified in EU action plans.77

However, EU policy makers have yet to tailor data policy

to accommodate conceptual challenges to health sector

development—for example, quality and performance

improvement—that fall within European legal competen-

cies and responsibilities in health.

Nevertheless, EC discussions on big data policy are still in

their infancy, as confirmed by communications with key EU

policy stakeholders. Additional progress in the merger

between big data policy and sectoral objectives may there-

fore be expected in the near future as the EC embarks on this

new field of policy. At this time, however, it remains unclear

how big data developments will advance health sector objec-

tives, casting doubt on optimistic predictions of the return on

big data investments in the EU.



No potential conflicts of interest were disclosed.


We would like to thank two anonymous sources within Euro-

stat (EC) for their insights and feedback on big data policy

developments within the EU.


This research was funded by LSE Health.


[1] European Commission. Communication from the Commis-

sion: a strategy for smart, sustainable and inclusive growth.

2010. COM/2010/2020. Available at http://eur-lex.europa.



[2] European Commission. Communication from the Commis-

sion: towards a thriving data-driven economy. 2014. COM/

2014/442. Available at


[3] European Commission. Communication from the Commis-

sion: open data: an engine for innovation, growth and trans-

parent governance. 2011. COM/2011/882. Available at


[4] European Commission. Communication from the Commis-

sion: a digital agenda for Europe. 2010. COM/2010/245.

Available at


[5] European Commission. Amendment of the directive on the

re-use of public sector information. 2013. Directive 2013/

37/EU. Available at


[6] Buchholtz S, Bukowski M, Sniegocki A. Big & open data in

Europe: a growth engine or a missed opportunity? 2014.

Available at (accessed 24 Janu-

ary 2015)

[7] Oberlander J. Big data and data science in Scotland: an

SSAC discussion document. 2014. Available at http://



(accessed 24 January 2015)

[8] European Commission. Press release: European commission

and data industry launch €2.5 billion partnership to master

big data. 2014. IP/14/1129. Available at


[9] European Commission. Memo: making the most of the data-

driven economy. 2014. MEMO/14/455. Available at http://

[10] European Commission. Press release: commission urges

governments to embrace potential of big data. 2014. IP/14/

769. Available at


[11] European Commission. Communication from the Commis-

sion: on effective, accessible and resilient health systems.

294 Health Systems & Reform, Vol. 1 (2015), No. 4

2014. COM/2014/215. Available at


[12] European Commission. Press release: research & innovation:

commission calls for partnerships to tackle societal chal-

lenges. 2011. IP/11/1059. Available at:


[13] European Commission. Directive on the re-use of public sec-

tor information. 2003. Directive 2003/98/EC. Available at


[14] Cabinet Office. Explanatory memorandum to the re-use of

public sector information regulations 2005. 2005; No. 1515.

Available at


[15] MIT Technology Review. The big data conundrum: how to

define it? 2013. Available at http://www.technologyreview.


(accessed 24 January 2015)

[16] De Mauro A, Greco M, Grimaldi M. What is big data? A

consensual definition and a review of key research topics.

In: 4th International Conference on Integrated Information:

2014 Sept 5-8; Madrid, Spain. AIP Conf Proc 2015;

1644:97-104. Available at:

aip/proceeding/aipcp/10.1063/1.4907823 (accessed 24 Janu-

ary 2015)

[17] Gandomi A, Haider M. Beyond the hype: big data concepts,

methods, and analytics. Int J Inf Manage 2015; 35(2): 137-


[18] Halamka JD. Early experiences with big data at an academic

medical center. Health Aff 2014; 33(7): 1132-1138.

[19] Howie L, Hirsch B, Locklear T, Abernethy AP. Assessing

the value of patient-generated data to comparative effective-

ness research. Health Aff 2014; 33(7): 1220-1228.

[20] Jee K, Kim GH. Potentiality of big data in the medical sec-

tor: focus on how to reshape the healthcare system. Healthc

Inform Res 2013; 19(2): 79-85.

[21] Margolis R, Derr L, Dunn M, Huerta M, Larkin J, Sheehan J,

Guyer M, Green ED. The National Institutes of Health’s Big

Data to Knowledge (BD2K) initiative: capitalizing on

biomedical big data. J Am Med Inform Assoc 2014; 21


[22] Roski J, Bo-Linn GW, Andrews TA. Creating value in health

care through big data: opportunities and policy implications.

Health Aff 2014; 33:1115-1122.

[23] High-Level Group for the Modernisation of Statistical Pro-

duction and Services. What does “big data” mean for official

statistics. 2013. Available at



dific (accessed 24 January 2015)

[24] Bellazzi R. Big data and biomedical informatics: a challeng-

ing opportunity. Yearb Med Inform 2014; 9(1):8-13.

[25] Feldman B, Martin EM, Skotnes T. Big data in healthcare:

hype and hope. 2012. Available at


scribd (accessed 17 August 2014)

[26] Hanlon A. Big data and the 5Vs. 2014. Available at http:// (accessed 24 January


[27] Morley-Fletcher E. Big data healthcare: an overview of the

challenges in data intensive healthcare. 2013. Available at

document.cfm?doc_id=3499 (accessed 17 August 2014)

[28] Fihn SD, Francis J, Clancy C, Nielson C, Nelson K, Rums-

feld J, Cullen T, Bates J, Graham GL. Insights from

advanced analytics at the veterans health administration.

Health Aff 2014; 33(7): 1203-1211.

[29] Fleurence RL, Beal AC, Sheridan SE, Johnson LB, Selby JV.

Patient-powered research networks aim to improve patient

care and health research. Health Aff 2014; 33(7): 1212-1219.

[30] Nambiar R, Bhardwaj R, Sethi A, Vargheese R. A look at

challenges and opportunities of big data analytics in health-

care. In: 2013 IEEE International Conference on Big Data:

2013 Oct 6–9; Santa Clara, CA, USA.

[31] Patil HK, Seshadri R. Big data security and privacy issues in

healthcare. In: 2014 IEEE International Congress on Big

Data; 2014 June 27–July 2; Anchorage, AK, USA.

[32] Yoon JP. Three-tiered data mining for big data patterns of

wireless sensor networks in medical and healthcare domains.

In: ICIW: The Eighth International Conference on Internet

and Web Applications and Services; 2013 June 23-28;

Rome, Italy.

[33] Poulymenopoulou M, Malamateniou F. Machine learning

for knowledge extraction from PHR big data. In: Integrating

information technology and management for quality of care,

Mantas J, Househ MS, Hasman A, eds. Amsterdam, The

Netherlands: IOS Press; 2014.

[34] Fallik D. For big data, big questions remain. Health Aff

2014; 33(7): 1111-1114.

[35] EPPI-Centre. EPPI-Centre methods for conducting system-

atic reviews. 2010. Available at


(accessed 24 January 2015)

[36] Groves P, Kayyali B, Knott D, Van Kuiken S. The “big data”

revolution in healthcare: accelerating value and innovation.

Center for US Health System Reform Business Technology

Office; 2013. Available at


[37] Bollier D. The promise and peril of big data. 2010. Available


Big_Data.pdf (accessed 17 August 2014)

[38] Lupse O, Crisan-Vida M, Stoicu-Tivadar L. Supporting

diagnosis and treatment in medical care based on big

data processing. In: Cross-border challenges in informat-

ics with a focus on disease surveillance and utilising big

data, Stoicu-Tivadar L, De Lusignan S, Orel A, Engel-

brecht R, Surj�an G, eds. Amsterdam, The Netherlands: IOS Press, 2014.

[39] Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M.

Behavioral intervention technologies: evidence review and

recommendations for future research. Gen Hosp Psychiatry

2010; 35(4): 332-338.

Salas-Vega et al.: Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU 295

[40] Kanza G, Babic A. Data mining in cancer registries: a case

for design studies. In: IFMBE Proceedings, Roa Romero

LM, ed. New York, NY: Springer; 2014.

[41] Kuriyan J, Cobb N. Forecasts of cancer & chronic patients:

big data metrics of population health. arXiv 2013. Available


[42] Savage N. Bioinformatics: big data versus the big C. Nature

2014; 509(7502): S66-S67.

[43] Schilsky RL, Michels DL, Kearbey AH, Yu PP, Hudis CA.

Building a rapid learning health care system for oncology:

the regulatory framework of CancerLinQ. J Clin Oncol

2014; 32(22): 2373-2379.

[44] Shaikh AR, Butte AJ, Schully SD, Dalton WS, Khoury MJ,

Hesse BW. Collaborative biomedicine in the age of big data:

the case of cancer. J Med Internet Res 2014; 16(4): e101.

[45] EHealthNews. Analytics and big data improve cancer treat-

ment at Italian institute. 2013. Available at http://www.

cancer-treatment-at-italian-institute (accessed 24 January


[46] McNeil C. CancerLinQ prototype demonstrates use of big

data in clinical care. ASCO Post 2013; 4(7). Available at,-2013/cancerlinq-


[47] Yu P, Artz D, Warner J. Electronic Health Records (EHRs):

Supporting ASCO’s vision of cancer care. In: ASCO Educa-

tional Book. Vol. 34, Dizon D, ed. Alexandria: American

Society of Clinical Oncology, 2014; 225–231.

[48] Gallagher J. Public Health England to launch largest cancer data-

base [internet]. BBC News. 2013. Available at (accessed 24 January 2015)

[49] Hodson J. Chan Soon-Shiong Institute for Molecular Medi-

cine and the University of Oxford establish partnership to

create the first center for genomic and proteomic medicine

in the United Kingdom to benefit NHS cancer patients. Reu-

ters. 2014. Available at


(accessed 24 January 2015)

[50] Twachtman G. Big data destined for the bedside within

5 years. Clinical Neurology News Digital Network. 2014.

Available at


935years/93a7ce09df79331c4a0da5c34e02305b.html (accessed

17 August 2014)

[51] Gibney E. Oxford big data centre to get 30 million. Times

Higher Education. 2013. Available at http://www.time-

30-million/2003661.article (accessed 24 January 2015)

[52] European Commission. Innovative Medicines Initiative 2.

2014. Available at

(accessed 24 January 2015)

[53] Hay SI, George DB, Moyes CL, Brownstein JS. Big data

opportunities for global infectious disease surveillance.

PLoS Med 2013; 10(4): e1001413.

[54] Nash DB. Harnessing the power of big data in healthcare.

Am Health Drug Benefits 2014; 7(2): 69-70.

[55] Ambord C, Favre F, Faeh D, Chiolero A. Public health sur-

veillance with big data: assessing diabetes trends using

medico-administrative data. Eur J Public Health 2014; 24(2)

Suppl: 353-354.

[56] Parliamentary Office of Science and Technology. Big data

and public health. 2014. Available at http://researchbriefings.

pdf (accessed 24 January 2015)

[57] Ola O, Sedig K. The challenge of big data in public health:

an opportunity for visual analytics. Online J Public Health

Inform 2014; 5(3): 1–21.

[58] Krumholz HM. Big data and new knowledge in medicine:

the thinking, training, and tools needed for a learning health

system. Health Aff 2014; 33(7): 1163-1170.

[59] Ellaway RH, Pusic MV, Galbraith RM, Cameron T. Devel-

oping the role of big data and analytics in health professional

education. Med Teach 2014; 36(3): 216-222.

[60] Etheredge LM. Rapid learning: a breakthrough agenda.

Health Aff 2014; 33(7): 1155-1162.

[61] Harper E. Can big data transform electronic health records

into learning health systems? In: Nursing Informatics 2014,

Saranto K, Weaver CA, Chang P, eds. Amsterdam, The

Netherlands: IOS Press; 2014.

[62] Pentland A, Reid TG, Heibeck T. Big data and health:

revolutionizing medicine and public health. 2013. Avail-

able at

WISH_BigData_Report.pdf (accessed 24 January 2015)

[63] Schultz T. Turning healthcare challenges into big data

opportunities: a use-case review across the pharmaceutical

development lifecycle. Bull Assoc Inf Sci Technol 2013; 39

(5): 34-40.

[64] Seebode C. BIG DATA infrastructures for pharmaceutical

research. In: 2013 IEEE International Conference, 2013 Oct

6-9; Silicon Valley, CA, USA. Available at http://ieeex-


[65] Abbott R. Big data and pharmacovigilance: using health

information exchanges to revolutionize drug safety. Iowa

Law Rev 2013; 99(1): 225-292.

[66] Gombocz E. Changing the model in pharma and health-

care—can we afford to wait any longer? in: Data Integration

in the life sciences, Baker CJO, Butler G, Jurisica I, eds.

New York: Springer; 2013.

[67] Lio X, Chen H. AZDrugMiner: an information extraction

system for mining patient-reported adverse drug events

in online patient forums. In: Smart health, Zeng D, Yang

CC, Tseng VS, Xing C, Chen H, Wang FY, Zheng X,

eds. New York: Springer; 2013.

[68] Szlez�ak N, Evers M, Wang J, P�erez L. The role of big data and advanced analytics in drug discovery, development, and com-

mercialization. Clin Pharmacol Ther 2014; 95(5): 492-495.

[69] Wolin B. Big data, pharmaceutical marketing and health-

care. J Digit Soc Media Mark 2014; 2(1): 35-39.

[70] European Commission. Innovative Medicines Initiative.

2014. Available at

mission (accessed 17 August 2014)

[71] Simpson SE, Madigan D, Zorych I, Schuemie MJ, Ryan PB,

Suchard MA. Multiple self-controlled case series for large-

scale longitudinal observational databases. Biometrics 2013;

69(4): 893-902.

296 Health Systems & Reform, Vol. 1 (2015), No. 4

[72] Darrow JJ, Avorn J, Kesselheim AS. New FDA break-

through-drug category: implications for patients. N Engl J

Med 2014; 370(13): 1252-1258.

[73] RaghupathiW, Raghupathi V. Big data analytics in healthcare:

promise and potential. Health Inf Sci Syst 2014; 2(3): 1-10.

[74] Emam K, Waldo A, Wright C. Webinar: Fear and loathing of

data monetization [internet]. Privacy Analytics. 2014. Avail-

able at


(accessed 24 January 2015)

[75] European Commission. Communication from the Commis-

sion: safeguarding privacy in a connected world: a European

data protection framework for the 21st century. 2012. COM/

2012/09. Available at


[76] European Commission. Directive on the protection of indi-

viduals with regard to the processing of personal data and on

the free movement of such data. 1995. Directive 95/46/EC.

Available at


[77] European Commission. Communication from the Commis-

sion: eHealth Action Plan 2012–2020. 2012. COM/2012/

736. Available at


[78] European Commission. Protection of personal data. 2015.

Available at

(accessed 24 January 2015)

[79] Fabian B, Ermakova T, Junghanns P. Collaborative and

secure sharing of healthcare data in multi-clouds. Inf Syst

2014; 48(2015): 132-150.

[80] Hsieh J-C, Li AH, Yang CC. Mobile, cloud, and big data

computing: contributions, challenges, and new directions in

telecardiology. Int J Environ Res Public Health 2013; 10

(11): 6131-6153.

[81] Sahoo SS, Jayapandian C, Garg G, Kaffashi F, Chung S,

Bozorgi A, Chen CH, Loparo K, Lhatoo SD, Zhang GQ.

Heart beats in the cloud: distributed analysis of electrophysi-

ological “big data” using cloud computing for epilepsy clini-

cal research. J Am Med Inform Assoc 2014; 21(2): 263-271.

[82] Youssef AE. A framework for secure healthcare systems

based on big data analytics in mobile cloud computing envi-

ronments. Int J Ambient Syst Appl 2014; 2(2): 1-11.

[83] Mowbray M, Pearson S, Shen Y. Enhancing privacy in cloud

computing via policy-based obfuscation. J Supercomput

2012; 61(2): 267-291.

[84] Pearson S, Shen Y, Mowbray M. A privacy manager for

cloud computing. In: Cloud computing, Jaatun MG, Zhao G,

Rong C, eds. New York: Springer; 2009.

[85] Gehrke J. Quo vadis, data privacy? Ann N Y Acad Sci 2012;

1260(1): 45-54.

[86] Kum HC, Krishnamurthy A, Machanavajjhala A, Reiter MK,

Ahalt S. Privacy preserving interactive record linkage

(PPIRL). J Am Med Inform Assoc 2014; 21(2): 212-220.

[87] Hare F. Borders in cyberspace: can sovereignty adapt to the

challenges of cyber security. In: The virtual battlefield: per-

spectives on cyber warfare, Czosseck C, Geers K, eds. The

virtual battlefield: perspectives on cyber warfare. Amster-

dam, The Netherlands: IOS Press; 2009.

[88] Jansen van Vuuren J, Leenen L, Zaaiman J. Using an ontol-

ogy as a model for the implementation of the national cyber-

security policy framework for South Africa. In: 9th

International Conference on Cyber Warfare & Security:

2014 Mar 24-25; West Lafayette. Available at http://www.



[89] Rubinstein IS. Big data: the end of privacy or a new begin-

ning? Int Data Priv Law 2013; 3(2): 74-87.

[90] Ganesh J. Big Data may be invasive but it will keep us

in rude health. Financial Times. 2014 Feb 21. Available


00144feab7de.html#axzz3qKZ9TXJK (accessed 17

August 2014)

[91] Triggle N. How did it go so wrong? 2014. Avail-

able at

(accessed 24 January 2015)

[92] Chawla NV, Davis DA. Bringing big data to personalized

healthcare: a patient-centered framework. J Gen Intern Med

2013; 28(3): S660-S665.

[93] Dilsizian SE, Siegel EL. Artificial intelligence in medicine

and cardiac imaging: harnessing big data and advanced com-

puting to provide personalized medical diagnosis and treat-

ment. Curr Cardiol Rep 2014; 16(1): 1-8.

[94] Asslaber M, Zatloukal K. Biobanks: transnational, European

and global networks. Brief Funct Genomics Proteomics

2007; 6(3): 193-201.

[95] Medtech Media. Leveraging big data and analytics in

healthcare and life sciences: enabling personalized medi-

cine for high-quality care, better outcomes. 2012. Avail-

able at


data-paper.pdf (accessed 17 August 2014)

[96] Boye N. Co-production of health enabled by next generation

personal health systems. In: pHealth 2012, Blobel B, Pharow

P, Sousa F, eds. Amsterdam, The Netherlands: IOS Press;


[97] Ginsburg GS, Willard HF. Genomic and personalized medi-

cine: foundations and applications. Transl Res 2009; 154(6):


[98] European Commission. Staff working document: use of

“-omics” technologies in the development of personalised

medicine. 2013. SWD/2013/436. Available at: http://ec.


[99] Fahim M, Idris M, Ali R, Nugent C, Kang B, Huh EN,

Lee S. ATHENA: a personalized platform to promote an

active lifestyle and wellbeing based on physical, mental

and social health primitives. Sensors (Basel) 2014; 14(5):


[100] Kim JH. Health avatar: an informatics platform for per-

sonal and private big data. Healthc Inform Res 2014; 20

(1): 1-2.

[101] Ashish N, Biswas A, Das S, Nag S, Pratap R. The Abzooba

Smart Health Informatics Platform (SHIP): from patient

experiences to big data to insights [internet]. arXiv 2012.

Available at

Salas-Vega et al.: Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU 297

[102] Lupton D. The commodification of patient opinion: the digi-

tal patient experience economy in the age of big data. Sociol

Health Illn 2014; 36(6): 856-869.

[103] Cheshire P, Lhotska L, Pharow P. Virtual physiological

human and its role for advanced pHealth service provision.

In: Blobel B, Pharow P, Parv L, editors. pHealth 2013. IOS

Press; 2013.

[104] Ji Z, Ganchev I, O’Droma M, Zhang X, Zhang X. A cloud-

based X73 ubiquitous mobile healthcare system: design and

implementation. Sci World J 2014; 2014(2014): 1-14.

[105] Jiang P. et al. An intelligent information forwarder for

healthcare big data systems with distributed wearable sen-

sors. IEEE Syst J 2014; PP(99): 1-13.

[106] Neill DB. Using artificial intelligence to improve hospital

inpatient care. IEEE Intell Syst 2013; 28(2): 92-95.

[107] Ghassemi M, Celi LA, Stone DJ. State of the art review: the

data revolution in critical care. Crit Care 2015; 19(118): 801.

[108] Baum S. 4 ways healthcare is putting artificial intelligence,

machine learning to use. MedCity News. 2015 [cited 2015

Feb 20]. Available at:



[109] Dixon BE, Rosenman M, Xia Y, Grannis SJ. A vision for the

systematic monitoring and improvement of the quality of

electronic health data. In: Lehmann CU, Ammenwerth E,

Nohr C, editors. MEDINFO 2013. IOS Press; 2013.

[110] Keenan GM. Big data in health care: an urgent mandate to

CHANGE nursing EHRs! Online J Nurs Inform 2014; 18(1):


[111] Lissovoy G. Big data meets the electronic medical record.

Med Care 2013; 51(9): 759-760.

[112] Vishnyakova D, Bottone S, Pasche E, Lovis C. Practical

implementation of a bridge between legacy EHR system and

a clinical research environment. In: Cross-border challenges

in informatics with a focus on disease surveillance and utilis-

ing big data, Stoicu-Tivadar L, de Lusignan S, Orel A,

Engelbrecht R, Surjan G, eds. Amsterdam, The Netherlands:

IOS Press; 2014.

[113] Hoffman S, Podgurski A. The use and misuse of biomedical

data: is bigger really better? Am J Law Med 2013; 39(2013):


[114] Skaliotis M. Timeliness and accuracy in official statistics

2.0. 2010. Available at

MichailSkaliotis_Eurostat.pdf (accessed 17 August 2014)

[115] European Commission. Communication from the Commis-

sion: together for health: a strategic approach for the EU

2008–2013. 2007. COM/2007/630. Available at http://ec.

[116] Phillips KA, Trosman JR, Kelley RK, Pletcher MJ, Douglas

MP, Weldon CB. Genomic sequencing: assessing the health

care system, policy, and big-data implications. Health Aff

2014; 33(7): 1246-1253.

[117] Kao RR, Haydon DT, Lycett SJ, Murcia PR. Supersize me:

how whole-genome sequencing and big data are transform-

ing epidemiology. Trends Microbiol 2014; 22(5): 282-291.

[118] Mavandadi S, Dimitrov S, Feng S, Yu F, Yu R, Sikora U,

Ozcan A. Crowd-sourced BioGames: managing the big data

problem for next-generation lab-on-a-chip platforms. Lab

Chip 2012; 12(20): 4102–4106.

[119] Swan M. Next-generation personal genomic studies: extend-

ing social intelligence genomics to cognitive performance

genomics in quantified creativity and thinking fast and slow

expanded context for personal genomics. In: 2013 AAAI

Spring Symposium: 2013 Mar 25-27; Palo Alto, CA, USA.

Available at


[120] Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G.

Big data in health care: using analytics to identify and man-

age high-risk and high-cost patients. Health Aff 2014; 33(7):


[121] Chute C, Ullman-Cullere M, Wood GM, Lin SM, He M,

Pathak J. Some experiences and opportunities for big data in

translational research. Genet Med 2013; 15(10): 802-809.

[122] Hofker MH, Fu J, Wijmenga C. The genome revolution and

its role in understanding complex diseases. Biochim Biophys

Acta 2014; 1842(10): 1889-1895.

[123] Lecroq T, Soualmia LF. From genome sequencing to bed-

side. Findings from the section on bioinformatics and trans-

lational informatics. Yearb Med Inf 2013; 2013(8): 175-177.

[124] Bromley D, Rysavy SJ, Su R, Toofanny RD, Schmidlin T,

Daggett V. DIVE: a data intensive visualization engine. Bio-

informatics 2014; 30(4): 593-595.

[125] Cole JB, Newman S, Foertter F, Aguilar I, Coffey M. Breed-

ing and genetics symposium: really big data: processing and

analysis of very large data sets. J Anim Sci 2012; 90(3):


[126] Costa FF. Big data in biomedicine. Drug Discov Today

2014; 19(4): 433-440.

[127] Dinov ID, Petrosyan P, Liu Z, Eggert P, Hobel S, Vespa P,

Woo Moon S, Van Horn JD, Franco J, Toga AW. High-

throughput neuroimaging-genetics computational infrastruc-

ture. Front Neuroinform 2014; 8(41): 1-11.

[128] Hood L. Systems biology and p4 medicine: past, present, and

future. Rambam Maimonides Med J 2013; 4(2): 1-15.

[129] Rosenstein BS, West CM, Bentzen SM, Alsner J, Andreas-

sen CN, Azria D, Barnett GC, Baumann M, Burnet N,

Chang-Claude J, et al. Radiogenomics: radiobiology enters

the era of big data and team science. Int J Radiat Oncol Biol

Phys 2014; 89(4): 709-713.

[130] Vivar JC, Pemu P, McPherson R, Ghosh S. Redundancy

Control in Pathway Databases (ReCiPa): an application for

improving gene-set enrichment analysis in omics studies and

“big data” biology. Omics 2013; 17(8): 414-422.

[131] Lueck R. Large-scale genome analysis on helix nebula: the

science cloud federated big data processing on-demand.

2014. Available at

files/6. EMBL flagship.pdf (accessed 17 August 2014)

[132] Keen J, Calinescu R, Paige R, Rooksby J. Big data + politics

= open data: the case of health care data in England. Policy

and Internet 2013; 5(2): 228-243.

[133] Itou T. Big data and official statistics: analysis of recent

discussions in statistical communities. 2014. Available at


298 Health Systems & Reform, Vol. 1 (2015), No. 4


Japan_0.pdf (accessed 17 August 2014)

[134] Baeza-Yates R. Big data or right data? 2013. Available at (accessed 17

August 2014)

[135] Curtis LH, Brown J, Platt R. Four health data networks illus-

trate the potential for a shared national multipurpose big

data network. Health Aff 2014; 33(7): 1178-1186.

[136] Edwards A, Hollin I, Barry J, Kachnowski S. Barriers to

cross-institutional health information exchange: a literature

review. J Healthc Inf Manag 2010; 24(3): 22-34.

[137] Elliot T, Holmes JH, Davidson AJ, La Chance PA, Nelson

AF, Steiner JF. Data warehouse governance programs in

healthcare settings: a literature review and a call to action.

eGEMS 2013; 1(1): 1–7.

[138] Karlberg M, Skaliotis M. Big data for official statistics:

strategies and some initial European applications. In:

Conference of European Statisticians: 2013 Sept 25-27;

Geneva, Switzerland. Available at



[139] Miller P. Genetic discrimination in the workplace. J Law

Med Ethics 1998; 26(3): 189-197.

[140] Riba S. The use of genetic information in health insur-

ance: who will be helped, who will be harmed and possi-

ble long-term effects. USC Rev Law Soc Justice 2007;

16(2): 469-489.

[141] Kum HC, Ahalt S. Privacy-by-design: understanding data

access models for secondary data. AMIA Jt Summits Transl

Sci Proc 2013; 2013(1): 126-130.

[142] European Commission. Communication from the Commission:

towards better access to scientific information: boosting the

benefits of public investments in research. COM/2012/401.

2012. Available at



[143] European Commission. Directive on the application of

patients’ rights in cross-border healthcare. Directive 2011/

24/EU. 2011. Available at


[144] European Commission. Communication from the Commis-

sion: on the protection of individuals with regard to the proc-

essing of personal data and on the free movement of such

data. COM/2012/11. 2012. Available at http://eur-lex.


[145] World Economic Forum & Boston Consulting Group.

Rethinking personal data: strengthening trust. 2012. Avail-

able at

kingPersonalData_Report_2012.pdf (accessed 17 August


[146] Megget K. Riding the data stream. PharmaTimes Digital.

2011. Available at

Riding_the_data_stream.aspx (accessed 24 January 2014)

[147] Gravelle H, Sutton M, Ma A. Doctor behaviour under a pay

for performance contract: treating, cheating and case find-

ing? Econ J 2010; 120(542): F129-F156.

[148] Sigfrid LA, Turner C, Crook D, Ray S. Using the UK pri-

mary care Quality and Outcomes Framework to audit health

care equity: preliminary data on diabetes management. J

Public Health (Oxf) 2006; 28(3): 221-225.

[149] Millett C, Netuveli G, Saxena S, Majeed A. Impact of pay

for performance on ethnic disparities in intermediate out-

comes for diabetes: a longitudinal study. Diabetes Care

2009; 32(3): 404-409.

[150] European Collaboration for Healthcare Optimisation

(ECHO). What is the ECHO project? 2014. Available at (accessed 24 January 2015)

[151] NHS England. The programme: better information

means better care. Technology, systems and data. 2014.

Available at

data/ (accessed 17 August 2014)

[152] Parliamentary Office of Science and Technology. Transpar-

ency of clinical trial data. 2014. Available at http://www.par (accessed 24

January 2015)

[153] Krumholz HM, Ross JS, Gross CP, Emanuel EJ, Hodshon B,

Ritchie JD, Low JB, Lehman R. A historic moment for open

science: the Yale University Open Data Access project and

medtronic. Ann Intern Med 2013; 158(12): 910-911.

[154] PMLiVE. Pharma industry announces data sharing com-

mitment. 2013. Available at


mitment_491944 (accessed 24 January 2015)

[155] Institute of Medicine. Sharing clinical trial data: maxi-

mizing benefits, minimizing risk. 2015. Available at

Data.aspx (accessed 24 January 2015)

[156] World Health Organization. WHO statement on public dis-

closure of clinical trial results. 2015. Available at http://

reporting_clinical_trials.pdf (accessed 24 January 2015)

[157] Audit Commission. Data remember: improving the

quality of patient based information in the NHS. 2002.

Available at


Reports/NationalStudies/dataremember.pdf (accessed

24 January 2015)

[158] Audit Commission. Data assurance framework. 2012.

Available at

tion-and-analysis/data-assurance-framework/ (accessed 24

January 2015)

[159] Spencer A. Hospital episode statistics (HES): improving the

quality and value of hospital data. 2011. Available at http://



(accessed 24 January 2015)

[160] Frisse M, Wilcox A, Sittig D, Kahn M, Lopez MH. Clinical

informatics, CER, and PCOR: building blocks for meaning-

ful use of big data in health care. In: Medical Care Special

Supplement Webinar Series (Electronic Data Methods)

Forum: 2012 Oct 31; Webinar. Available at http://reposi-

Salas-Vega et al.: Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU 299

[161] Rial N. The power of big data in Europe. New Europe. 2013.

Available at

europe (accessed 17 August 2014)

[162] European Commission. Improving health reporting mecha-

nisms. Public health. 2015. Available at http://ec.europa.


(accessed 24 January 2015)

[163] European Health Forum Gastein. Conference report:

resilient and innovative health systems for Europe.

2013. Available at


(accessed 24 January 2015)

[164] European Commission. Press release: better use of health

data will transform the healthcare landscape, says expert

report. 2012. IP/12/453. Available at


[165] European Commission. Communication from the Commis-

sion: a comprehensive approach on personal data protection

in the European Union. 2010. COM/2010/609. Available at


[166] Diverty B. Drowning in data? Health analytics, a lifejacket

for performance measurement. 2014. Available at http:// Diverty Deck.

pdf (accessed 17 August 2014)

[167] Kaplan RM, Chambers DA, Glasgow RE. Big data and large

sample size: a cautionary note on the potential for bias. Clin

Transl Sci 2014; 7(4): 342-346.

[168] Lamarche-Vadel A, Pavillon G, Aouba A, Johansson LA,

Meyer L, Jougla E, Rey G. Automated comparison of last

hospital main diagnosis and underlying cause of death

ICD10 codes, France, 2008–2009. BMC Med Inform Decis

Mak 2014; 14(44): 1-9.

[169] Harron K, Wade A, Gilbert R, Muller-Pebody B, Goldstein

H. Evaluating bias due to data linkage error in electronic

healthcare records. BMC Med Res Methodol 2014; 14(36):


[170] European Parliament & The Council. Decision: establishing

a second programme of community action in the field of

health. 2007. Decision No 1350/2007/EC. Available at


[171] EuropeanCommission. European core health indicators (ECHI).

Public health. 2015. Available at

cators/echi/index_en.htm (accessed 24 January 2015)

[172] Accenture. Global market for electronic health records

(EHR) expected to reach $22.3 billion by the end of 2015.

2014. Available at



ture.htm (accessed 24 January 2015)

[173] Todd R. E-Health Insider: EMIS and TPP share data. ehi

News. 2013. Available at

EHI/8458/emis-and-tpp-share-data (accessed 24 January


[174] Berndt DJ, Fisher JW, Hevner AR, Studnicki J. Healthcare

data warehousing and quality assurance. Computer (Long

Beach Calif) 2001; 34(12): 56-65.

[175] Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K,

Pavel M, Riley WT, Shar A, Spring B, Spruijt-Metz D, et al.

Mobile health technology evaluation: the mHealth evidence

workshop. Am J Prev Med 2013; 45(2): 228-236.

[176] Kayyali B, Knott D, Kuiken S Van. The big-data revo-

lution in US health care: accelerating value and innova-

tion. 2013. Available at


tion_in_us_health_care (accessed 17 August 2014)

[177] Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Rox-

burgh C, Byers AH. Big data: the next frontier for inno-

vation, competition, and productivity. 2011. Available at

big_data_the_next_frontier_for_innovation (accessed 24

January 2015)

[178] Hervey T, Vanhercke B. Health care and the EU: the law and

policy patchwork. In: Mossialos E, Permanand G, Baeten R,

Herv TK, editors. Health systems governance in Europe: the

role of EU law and policy. New York: Cambridge University

Press; 2010.

300 Health Systems & Reform, Vol. 1 (2015), No. 4

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