Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy

Considerations for Health Informatics Researchers

Laura Moss

1,2

, Martin Shaw

, Ian Piper

, Christopher Hawthorne

and John Kinsella

Department of Anaesthesia, Pain & Critical Care, School of Medicine, University of Glasgow, Glasgow, U.K.

Department of Clinical Physics, NHS Greater Glasgow & Clyde, Glasgow, U.K.

Department of Neuroanaesthesia, NHS Greater Glasgow & Clyde, Glasgow, U.K.

Keywords:

Big Data, Privacy, Critical Care, Data Analysis, Trust, Data Security.

Abstract:

Advances in technology has transformed clinical medicine; electronic patient records routinely store clinical

notes, internet-enabled mobile apps support self-management of chronic diseases, point-of-care testing enables

laboratory tests to be performed outside of hospital environments, patient treatment can be delivered over wide

geographic areas and wireless sensor networks are able to collect and send physiological data. Increasingly,

this technology leads to the development of large databases of sensitive electronic patient information. There

is public interest into the secondary use of this data; many concerns are voiced about the involvement of private

companies and the security and privacy of this data, but at the same time, these databases present a valuable

source of clinical information which can drive health informatics and clinical research, leading to improved

patient treatment. In this position paper, we argue that for health informatics projects to be successful, public

concerns over the secondary use of patient data need to be addressed in the design and implementation of the

technology and conduct of the research project.

1 INTRODUCTION

Healthcare is rapidly changing and advanced technol-

ogy is enabling the collection of vast amounts of pa-

tient data. Consequently healthcare is experiencing

a Big Data phenomenon. Whilst health informatics

research is focused on the development of novel ap-

proaches to enable the intelligent analysis of this data,

the use of electronic patient data to advance these ap-

proaches and the implementation of these technolo-

gies within real world healthcare environments raises

many ethical questions and divides public opinion.

In this paper we explore some of the issues regard-

ing the use of electronic patient data for secondary

purposes, speciﬁcally trust, security and patient con-

ﬁdentiality. The paper is organised as follows: section

2 describes the prominence of Big Data within health-

care and challenges faced; section 3 discusses issues

raised in the sharing of data, in particular the involve-

ment of private companies and views of the general

public; section 4 illustrates real-world issues faced

whilst implementing a big data analysis platform into

a healthcare environment; section 5 discusses consid-

erations and opportunities for health informatics re-

searchers; ﬁnally, section 6 concludes the discussion.

2 BACKGROUND

As technology advances, data is increasingly col-

lected through a variety of mechanisms. It is esti-

mated that 2.5 quintillion bytes of data is currently

generated each day (IBM a, 2016). Big Data is a term

which is now commonly used to describe such large

and complex datasets. Advanced analytics are often

applied to Big Data to extract meaning, insights and

discovery of new knowledge. However, using these

large datasets presents challenges due to the data’s

volume (many sources e.g. sensors, social network-

ing), variety (many formats, e.g. videos, text) and ve-

locity (speed at which data is produced and require-

ments for near-real time processing). Other attributes

can include veracity (noisy, messy data), variability

(meaning of the data can be constantly shifting) and

ﬁne-grained (Kitchen and McArdle, 2016).

Within healthcare, the increasing use of technol-

ogy is creating large volumes of clinical data; in 2011,

the global size of healthcare data was estimated to be

161 billion gigabytes (IBM b, 2016). This inﬂux of

data is from a variety of technical advances: e.g. en-

hanced clinical imaging, electronic medical records,

and physiological sensors. Additionally, the growth

Moss L., Shaw M., Piper I., Hawthorne C. and Kinsella J.

Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy Considerations for Health Informatics Researchers.

DOI: 10.5220/0006251504630468

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 463-468

ISBN: 978-989-758-213-4

463

of wearable sensor devices (e.g. smart watches) is

leading to patients themselves being able to generate

large amounts of healthcare data.

The potential clinical beneﬁt of using this data

is well established. Analysis of healthcare data can

drive improvement in many areas, including: clini-

cal and organisation processes, optimisation of treat-

ments, and reduction of healthcare costs through in-

tervention at an earlier point and more proactive and

targeted care. Patient data can be used to predict clin-

ical risk, targeting resources where they are needed

most and identifying problems that would beneﬁt

from early intervention. Technologies that aid clin-

ical decision-making and help clinicians to manage

the exponential growth in medical knowledge offer

substantial opportunities to reduce variation, improve

the quality of care, and possibly lower costs (Jaspers

et al., 2011), (Fillmore et al., 2013).

Whilst a thriving health informatics community is

focused on the development of novel approaches and

tools to enable the intelligent and sophisticated anal-

ysis and use of healthcare data, the transfer of these

technologies and ideas to real world clinical environ-

ments faces many challenges. Healthcare providers

often do not have access themselves to data sci-

ence expertise, required technology infrastructure and

funding to do this themselves. In many cases, to en-

able the potential for big data analysis to improve

healthcare, partnerships have to be formed between

healthcare providers, research organisations and pri-

vate companies. High proﬁle examples include Deep-

Mind and IBM Watson technology. IBM’s Watson is

being used within oncology in US and Canadian hos-

pitals to assess tumours (IBM c, 2016) and DeepMind

is using patient data from UK hospitals to develop

diagnostic tools in areas such as acute kidney injury

(DeepMind a, 2016) and ophthalmology (DeepMind

b, 2016).

3 TRUST, SECURITY AND

PRIVACY

Trust and conﬁdentiality between a clinical provider

and patient is not new: it is central to the practice

of healthcare and has been focused on since Hip-

pocrates. Whilst the concept of patient conﬁdential-

ity has endured as an ideal throughout history, the

precise nature of it has changed with the sociohis-

toric context (for a detailed review see (A.Ferguson,

2012)). In the digital age, patient conﬁdentiality is

often framed within the context of electronic patient

records and the potential involvement of third parties.

Whilst the involvement of private organisations and

other research organisations can resolve many prac-

tical issues for healthcare providers, it often involves

the transfer of sensitive patient data to these organ-

isations for research development and this can raise

many questions. For example:

• Should electronic patient data be used for sec-

ondary research purposes?

• Who does the electronic patient data belong to?

• Who should be able to use the electronic patient

data? Public sector healthcare organisations (e.g.

NHS) and/or commercial companies?

• Who is collecting the data and where is it stored?

• What safeguards should be put in place to protect

patient conﬁdentiality?

• How do patients and relatives feel about the col-

lection and use of this data?

Most countries have some form of regulation or

processes which have been legally put in place to re-

solve some of the above issues. Additionally, insti-

tutions often have internal procedures and practices

in place to protect patient data. However, public

perception of the adequacy of these frameworks can

be divided and concerns raised about whether pri-

vate companies, in particular, can be trusted with pa-

tient data. For example, the public sector can be per-

ceived as more trustworthy than proﬁt-making organ-

isations when using patient data (Focus Groups et al.,

2013). Recent publicity in cases such as DeepMind’s

arrangement with the Royal Free Hospital, London,

has been controversial and highlighted the require-

ment for more robust safeguards to be put in place to

ensure patient data is adequately secured (New Sci-

entist, 2016). Additionally, there are constant reports

in the media about data leaks and unsuccessful, large-

scale, health I.T projects (Presser et al., 2015).

It also raises issues about whether patient con-

sent and public awareness of healthcare data shar-

ing is adequate. A number of studies have identiﬁed

that there is low awareness by the general public of

electronic patient record systems and how and why

healthcare data might be used (BMA, 2016) (Rior-

dan et al., 2015). Riordan et al (Riordan et al., 2015)

found that most people would prefer to opt-in before

their identiﬁable records were used and half of partic-

ipants would share their de-identiﬁed records under

implicit consent. A recent consultation with the gen-

eral public in the UK on this issue identiﬁed that they

had little conﬁdence in the safeguards put in place to

protect data. Additionally, it was felt that there was

a lack of accountability within the system, and ma-

licious use of data by private companies (e.g. phar-

maceutical and insurance) was a concern for many

HEALTHINF 2017 - 10th International Conference on Health Informatics

464

people (BMA, 2016). Lessons learnt from the fail-

ure of large scale patient data sharing projects show

the requirements for clear communication to the pub-

lic, easy to understand consent rules and strong over-

sight and communication regarding distribution and

use of patient data (Presser et al., 2015). For a more

detailed systematic review of the literature regarding

public responses to sharing of health data see (Aitken

et al., 2016b).

To investigate the opinions of the general pub-

lic further, in a small study we asked the audience

at a science festival their thoughts on the topic of

secondary use of patient data (Kinsella et al., 2017).

Questions covered included: whether the participants

were aware of the potential of using their medical

data for secondary research purposes; whether pa-

tient data should be used for research purposes and

how likely would they be themselves to share their

own personal data for research; if they trust clinicians

with their data; and their opinions on the role of pri-

vate/commercial companies in supporting and/or car-

rying out research on their own medical data. 39 out

of 41 adults responded to the survey (from which we

have full results for 37 adults). Table 1 shows re-

sults from the survey. The vast majority of respon-

dents felt that their medical data should be used for

research purposes and would be happy to share their

data. This is in keeping with a number of other studies

(as summarised in (Aitken et al., 2016b). Addition-

ally, most respondents trusted clinicians, but when it

came to private companies, the response was mixed.

This difference in perceived trustworthiness between

clinicians and private companies has been found pre-

viously (e.g.(Aitken et al., 2016a),(BMA, 2016)).

We also looked for any age divide in the partici-

pant’s responses. We asked which age category par-

ticipants fell into (21 and under, 22-34, 35-44, 45-

54, 55-64, 65 and over) and three questions regarding

the involvement of private companies: 1) Would you

trust a private company to do research with your med-

ical data? 2) Would you trust a private company to

do research with anonymous medical data? 3) Would

you be comfortable with a private company provid-

ing the support to medical researchers to enable them

to do medical research? Tables 2 and 3 display the

results of this analysis. In both age groups, more peo-

ple had positive responses (agreed or strongly agreed)

than negative ones (disagree or strongly disagree). Al-

though younger people are often thought to be more

conﬁdent with technology and data sharing, no siz-

able difference between the two age groups was found

in this study. A larger sample size would be required

to show statistical signiﬁcance.

Public opinion and perception of the use of health-

care data can be divided and it is clear from these stud-

ies and the opinions of other researchers (e.g. (van

Staa et al., 2016)), that for health informatics projects

involving transfer and analysis of patient data by third

parties to be a success, the trust of the general public

needs to be earned and respected by all involved.

4 CHART-ADAPT CASE STUDY

To illustrate some of the issues which may need to

be considered in collaborative health informatics re-

search projects, in this section we discuss some of the

actions which the CHART-ADAPT project instigated

to try and overcome data sharing concerns (CHART-

ADAPT, 2016).

The CHART-ADAPT platform allows the fast

analysis of high and low frequency data collected

from a critical care unit; enabling the creation and

assessment of novel, closed loop, diagnostic or ther-

apeutic models and algorithms. Routinely recorded

patient data is automatically transferred from the elec-

tronic patient record system in the critical care unit to

a high performance computing platform implement-

ing a Spark (Apache a, 2016), Scala (Scala, 2016) and

Hadoop (Apache b, 2016) technology stack. Complex

physiological algorithms are then applied to the data

to derive clinically useful variables which are returned

back into the clinical environment and integrated with

the existing electronic patient record system.

A lack of the required technical infrastructure

within the hospital to process the patient data within

clinically meaningful timescales meant it was es-

sential for the healthcare provider and academic re-

searchers to form a collaborative team which included

commercial partners to provide the required high per-

formance computing infrastructure.

Due to the nature of the collaboration and the re-

quirement to transfer patient data, the project team

was aware of the need to maintain patient conﬁ-

dentiality, the governance of patient data, and the

need to gain the conﬁdence of patients and unit

staff. Several concerns were identiﬁed: 1) failure of

the de-identiﬁcation software and subsequent trans-

fer of identiﬁable patient data outside the health-

care provider’s network, 2) secure handling of the

anonymised patient data by the commercial partner,

3) correct re-identiﬁcation of patient data when it re-

entered the healthcare provider’s network, and 4) pub-

lic perception of a commercial partner supporting the

patient data analysis. These concerns were considered

from the start of the project and all the collaborating

organisations worked together to integrate the follow-

ing activities into the project plan:

Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy Considerations for Health Informatics Researchers

465

Table 1: General Public Opinions on Secondary Healthcare Use.

Question Yes No

Are you aware that medical data could

be used for research?

30 (81.2%) 7 (18.9%)

Question Strongly agree/agree Undecided Disagree/Strongly disagree

Medical data should be used for re-

33 (89.2%) 4 (10.8%) 0

Would you be happy to share your

healthcare data?

31 (83.8%) 4 (10.8%) 2 (5.4%)

Do you trust clinicians with your

healthcare data?

27 (73%) 7 (18.9%) 3 (8.1%)

Do you trust private companies to use

your medical data for research pur-

poses?

8 (21.6%) 20 (54%) 9 (24.3%)

Table 2: Responses < 35 years old, Total = 21 respondents.

Question Strongly Agree Agree Undecided Disagree Strongly Disagree

1 1 (4.8%) 7 (33.3%) 10 (47.6%) 2 (9.5%) 1 (4.8%)

2 4 (19%) 8 (38.1%) 7 (33.3%) 1 (4.8%) 1 (4.8%)

3 5 (23.8%) 10 (47.6%) 5 (23.8%) 0 (0%) 1 (4.8%)

Table 3: Responses >= 35 years old, Total = 16 respondents

Question Strongly Agree Agree Undecided Disagree Strongly Disagree

1 1 (6.25%) 3 (18.8%) 8 (50%) 2 (12.5%) 2 (12.5%)

2 2 (12.5%) 7 (43.8%) 5 (31.3%) 2 (12.5%) 0 (0%)

3 4 (25%) 5 (31.3%) 6 (37.5%) 1 (6.25%) 0 (0%)

• Regulatory approval (beyond minimum require-

ments) was acquired for the transfer of pa-

tient data (e.g. NHS Research Ethics, Caldicott

Guardian approval)

• The project developed software to automatically

anonymise the patient data before it left the

healthcare environment. A rigorous testing plan

was followed and repeated at regular intervals to

ensure conﬁdentiality was maintained.

• The commercial partner responsible for techni-

cal support of the data analysis (Aridhia) devel-

oped an Information Governance Strategy for the

project which made explicit the data handling and

security procedures put in place. Close communi-

cation was also maintained between the personnel

responsible for Information Governance in both

organisations (healthcare and commercial).

• Public engagement initiatives were implemented.

For example, posters and leaﬂets were made avail-

able in the unit, staff were briefed and updated on

project progress, and a public event was hosted to

discuss patient data sharing within critical care.

• Attendance at relevant academic and healthcare

events was scheduled into the project. This gave

the team the opportunity to discuss the platform

and gather feedback which was fed into the devel-

opment of the project.

Although some activities were time-consuming

and beyond the usual scope of a research project, it

was beneﬁcial not only for development of the plat-

form, but also to make sure, to aid future acceptance

of the technology, that we took the clinical staff and

general public with the project, rather than exclude

them and present the technology as a fait accompli.

5 OPPORTUNITIES FOR

HEALTH INFORMATICS

RESEARCH

Whilst public opinion on trust, security and privacy

of patient data needs be carefully considered in re-

search projects, there is also an opportunity for the

health informatics community to develop tools and

technologies to address these concerns. Below are

some suggestions (although this is not exclusive) and

comments on how the health informatics community

may be able to contribute:

• Communication of Health Informatics Projects

- There is a need to develop clear, concise, up-to-

HEALTHINF 2017 - 10th International Conference on Health Informatics

466

date summaries of health informatics projects to

aid transparency, in particular regarding the use

of patient data. Researchers should consider how

they will engage the public when designing and

implementing the health informatics project.

• Dynamic Consent - Current mechanisms of in-

formed consent for patient data sharing are static,

paper-based and designed around legal frame-

works and regulation. They are also speciﬁc to in-

dividual research studies and have to be repeated

for subsequent studies. There is a growing aware-

ness that this is inadequate and future policies are

moving towards a more sophisticated form of con-

sent (e.g. the proposed EU General Data Protec-

tion Regulation (GDPR, 2016)). Dynamic con-

sent provides patients with a personalised inter-

face, placing them in control of how their health-

care data is used; data sharing preferences can

be stated and often they can view how their data

is being used by researchers (Kaye et al., 2015),

(Williams et al., 2015). Once consent has been

speciﬁed by patients, new tools and technologies

are required which enable their preferences to

be dynamically and automatically applied across

multiple clinical databases and research studies.

• Safe Havens - To control how electronic pa-

tient data is used by researchers, many healthcare

providers are making it accessible through Safe

Havens (i.e. it doesn’t leave an authorised envi-

ronment) (Caldicott, 2016). Safe Havens pull to-

gether data from multiple healthcare sources and

links made between the datasets whilst maintain-

ing patient conﬁdentiality. Safe Havens require a

suite of software tools to: ensure security of the

centrally stored data (e.g. defend against cyber at-

tacks), enforce data access controls, and audit the

use of the patient data. Whilst basic tools have

been implemented, there is still potential for more

sophisticated software to support these activities.

• De-identiﬁcation of Patient Data - Generally,

there is public support for the sharing of de-

identiﬁed data for research purposes. National

and international guidelines specify methods for

de-identiﬁcation and can include the removal or

generalisation of certain attributes. Experts can

also be asked to identify attributes with an associ-

ated risk leading to patient identiﬁcation. As re-

moval of data can lead to a lack of quality of the

dataset overall, there is a balance to be struck be-

tween usability and patient conﬁdentiality. This is

a non-trivial optimisation problem which comput-

ing and artiﬁcial intelligence ﬁelds are well placed

to contribute towards workable solutions.

• Re-identiﬁcation of Patient Data - Even when

patient data has been de-identiﬁed, there is still a

possibility that it can be re-identiﬁed through the

use of other, publicly available, datasets. This is

likely to be a growing concern, especially with

initiatives to make more data available and ma-

chine readable (e.g. Semantic Web). Some so-

lutions to reduce the chances of this happening

include: removal of high risk variables from a

dataset (e.g. features which are available in multi-

ple documents and publicly available); and gener-

alisation of patient data into ‘bins’ of data (e.g.

values are generalised over 5 patients). Again,

computing and artiﬁcial intelligence ﬁelds are

well placed to develop tools which enable the au-

tomatic identiﬁcation of high risk attributes.

6 CONCLUSION

The health informatics community has an important

role to play in the development of novel technology

and algorithms to enable advances in clinical knowl-

edge and the quality of patient care. This type of re-

search requires access to sufﬁcient volumes of patient

data which raises important issues by the general pub-

lic regarding ethics, trust and security of patient data,

especially if private companies are involved in the re-

search activities. Our position is that, despite these

concerns, it is necessary for private companies, re-

search institutions and healthcare providers to work

together to successfully transition technology projects

from research to real-world environments. However,

it is vital that patient conﬁdentiality is maintained dur-

ing all stages of development. There is a role for

policy makers to ensure that existing legislation and

procedures are adequate for a fast moving technology

industry and that there is clear accountability. Addi-

tionally, there needs to be greater public engagement

on health informatics projects and open communica-

tion regarding the potential use of their data.

ACKNOWLEDGEMENTS

The CHART-ADAPT project is a collaboration be-

tween the University of Glasgow, Aridhia, NHS

Greater Glasgow & Clyde, and Philips Healthcare.

It has been co-funded by Innovate UK (ref:102113).

Approval for the CHART-ADAPT work was granted

by the West of Scotland Research Ethics Committee

(REC ref: 15/WS/0222) and local Caldicott Guardian

approval has been provided. Approval for the sur-

vey (section 4) was provided by MVLS, University of

Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy Considerations for Health Informatics Researchers

467

Glasgow. The CHART-ADAPT project we would like

to acknowledge the staff and patients of the Neuroin-

tensive care unit, Neurosciences Institute, Glasgow.

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