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Health Systems & Reform

ISSN: 2328-8604 (Print) 2328-8620 (Online) Journal homepage: http://www.tandfonline.com/loi/khsr20

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

CONTENTS

Introduction

Defining Big Data

Methods

Data Sources and Search Strategy

Results

Ongoing Initiatives

Challenges Surrounding Use of Big Data in Health Care

Discussion

A Balanced Framework Encompassing Data Confidentiality

and Use

Integration of Data Systems

Ensuring Quality of Data Collection, Analysis, and Oversight

Untangling Hype from Reality

Conclusions

References

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.

INTRODUCTION

“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 initiative 1 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: [email protected]

285

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 data 5,13,14

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

Commission. 11,12

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.

DEFINING BIG DATA

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-

ity 24,25

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.

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.

DATA SOURCES AND SEARCH STRATEGY

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

included.

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.

287

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.

RESULTS

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.

ONGOING INITIATIVES

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: 1 st + 2

nd ) PM: (big data [tiab]) OR (big data [tw])

GS/EL: (“big data”)

S: (“big data” [title/abs/keyw])

AND

String 2: health care (search level: 1 st + 2

nd ) 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])

AND

String 3: policy (search level: 2 nd ) 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

development. 70

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 communications 2,75

and directives 5,76

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 clouds 22,79-82

that make use of privacy

mechanisms such as obfuscation 80,83,84

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 data 89

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 care.data, 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 comparisons 115

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 sequencing 117-119

to boost genetic profiling capacity.

Sequencing and translational bioinformatics 24,120-123

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

CHALLENGES SURROUNDING USE OF BIG DATA

IN HEALTH CARE

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 data 34,139,140

—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 health 142

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 transparency 89

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 information 63

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 performance 132

or may actively work to

game data return through exception reporting 147,148

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 care.data program serve as two

examples. 150,151

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 population 34

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 2001 157

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

level.

Interoperability

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

languages. 134

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 needed 138

and may facilitate access and

effective data use.

DISCUSSION

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.

A BALANCED FRAMEWORK ENCOMPASSING

DATA CONFIDENTIALITY AND USE

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

INTEGRATION OF DATA SYSTEMS

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 factors 92,99

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-

tives 5,13

—gain prominence. 173

ENSURING QUALITY OF DATA COLLECTION,

ANALYSIS, AND OVERSIGHT

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

initiatives.

Notably, the EU is currently witnessing the arrival of

affordable personal data-generating devices 80,104

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 data 61

may,

however, ultimately define collection and use of information

available through this medium.

UNTANGLING HYPE FROM REALITY

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

data.

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.

CONCLUSIONS

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.

DISCLOSURE OF POTENTIAL CONFLICTS OF

INTEREST

No potential conflicts of interest were disclosed.

ACKNOWLEDGMENTS

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.

FUNDING

This research was funded by LSE Health.

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http://newsroom.accenture.com/news/global-market-for-electronic-health-records-expected-to-reach-22-3-billion-by-the-end-of-2015-according-to-accenture.htm

http://www.ehi.co.uk/news/EHI/8458/emis-and-tpp-share-data

http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care

http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation