Medical Data, Reconciling Research and Data Protection

Abstract

Most of our medical records are being processed electronically, centralised and easily accessible. In this paper, we will discuss the advantages of the system for research, as well as its potential challenges.

Full text

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© University Press 2022
ISSN 2719-6550
ISSN 2719-7417 online
“Journal of Education, Technology and Computer Science”
No. 3(33)/2022
www.eti.ur.edu.pl
Received: 2.09.2022 DOI: 10.15584/jetacomps.2022.3.14
Accepted for printing: 10.11.2022
Published: 22.12.2022
License: CC BY-SA 4.0
CHRISTINE HILCENKO 1,2,3, TARA TAUBMAN-BASSIRIAN4
Medical Data, Reconciling Research and Data Protection
1 ORCID: 0000-0002-9596-7833, Ph.D., Cambridge Institute for Medical Research, Cambridge,
CB2 0XY, UK.
2 Department of Haematology, University of Cambridge, Cambridge, CB2 0XY, UK
3 Wellcome Trust-Medical Research Council Stem Cell Institute, University of Cambridge, Cam-
bridge, UK
4 https://www.datarainbow.eu
Abstract
Most of our medical records are being processed electronically, centralised and easily acces-
sible. In this paper, we will discuss the advantages of the system for research, as well as its poten-
tial challenges.
Keywords: health data, privacy, data protection, GDPR, security, information, research, EMR,
EHR, European Data Hub
I. The purpose and benefits of digital health data
1. Electronic Health Record (EHR) or Electronic Medical Record
(EMR)
An electronic medical record (EMR) is a digital version of a chart with pa-
tient information stored in a computer and an electronic health record (EHR) is
the systematised collection of patient and population electronically stored health
information in a digital format. These records are shared through different coun-
tries and different medical departments. An EHR is a digital version of a pa-
tient’s paper chart. EHRs are real-time, patient-centred records that make infor-
mation available instantly and securely to authorised users. Hand-written data is
replaced with electronic records to maintain the constant flow of patient-related
data. Introduced in 2009, Today, over 80% of points of medical care are using
this technology for billing and entry point of medical information. EHRs are
built to easily share information with other health care providers and organisa-

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tions – such as laboratories, specialists, medical imaging facilities, pharmacies
allowing better diagnostics and patient outcomes based on the patient’s medical
history. It also improves patient participation, care coordination, and is cost sa-
ving. For instance, EHR alerts can be used to notify providers when a patient has
visited the hospital, allowing them to proactively follow up with the patient.
Every health provider can have the same accurate and up-to-date information
about a patient. This is especially important with patients who are seeing multi-
ple specialists, receiving treatment in an emergency setting or making transitions
between care settings.
It can reduce medical errors and unnecessary tests and reduce the chance
that one specialist will not know about an unrelated (but relevant) condition
being managed by another specialist. An EHR not only keeps a record of a pa-
tient’s medications or allergies, it also automatically checks for problems when-
ever a new medication is prescribed and alerts the clinician to potential conflicts.
It can help providers quickly and systematically identify and correct operational
problems. In a paper-based setting, identifying such problems is much more
difficult, and correcting them can take years.
2. Individual patients’ electronic records processing for wider scientific
research projects
In data protection terms when data collected for one purpose is then de-
ployed for a new, unrelated, purpose, we are engaging in secondary processing.
The secondary processing of personal health data for scientific research in
the medical field is coveted for research and new treatments that improve public
health. Its legal status, however, is far from unproblematic. An unclear status in
law of data reuse is compounded by a shift in research towards decentralised
clinical trials: deployment of new artificial intelligence technologies is leading to
a rethinking of biomedical research from what was traditionally organised. This
shift is all the more important as classical research tends to be questioned fol-
lowing various health scandals. Fewer people are willing to participate in medi-
cal trials. In digital research, AI tools are preferably deployed in the exploitation
of databases to explore patient records, via medical imaging or connected medi-
cal devices. The analysis of the various data sets with more efficient computing
capabilities makes it possible to increase the speed of discoveries, acting as an
accelerator of research. Clinical procedures are simplified, time is spared, and,
above all, the physical integrity of individuals is preserved. This shift, for in-
stance, has allowed discovery of an antibiotic molecule capable of bypassing
antibiotic resistance in February 2020 (Internet 1; Internet 2).
The primary advantage of digitalising research data is the increased ease of
participation in research. Many studies struggle to recruit and retain sufficient
participants. Less and less patients take part in clinical trials. A survey by CISCRP

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(Internet 3) indicates that physical distance to research sites is one of the main
barriers to increased participation.
The second advantage is the amount of data from a larger number of partici-
pants that a decentralized study can generate. Finally, decentralized clinical trials
attract a more inclusive variety of participants. If the advantages are obvious,
they are not inconvenient. Apart from the digital divide excluding the popula-
tions with less access to technology, data quality, security and strong medical
ethics are required. Big data analytics, based on larger data sets, using tools or
algorithms are big assets for medical and scientific research. Paolo and Binco-
letto (2021) examine the secondary processing of personal health data for scien-
tific research in the medical field. Looking at the controllers’ obligations to
comply with the data protection framework to safeguard fundamental rights and
freedoms. After comparing the implementation of EU regulation into the French
and Italian national legislations, they propose “a proactive, legal-technical
e-health solution that complies with the rules and principles of the legal frame-
works and empowers the individual’s control over personal health data while
promoting medical research. To this end, the data protection by design concept
plays a central role, and an interdisciplinary approach is fundamental in combi-
ning legal and technical perspectives”.
The case of Yoti illustrates the potentials for research and dangers for
the data (Internet 4)
For many years, immigration departments have struggled with physical de-
termination of the age immigrants claiming to be minor. Such determination by
physical examination have never been accurate. The algorithm the company Yoti
has created claims to have achieved the goal with high accuracy. For doing so,
a large world wide database of children aged between 12 and 19 years old. Has
be created. Admittedly some parents have received financial compensation in
exchange of pictures of their children with month and year of birth. By creating
a data set of children of various world ethnic faces, the algorithm can determine
the face of a minor. So far, the software has been commercialised in supermar-
kets for the sale of alcohol or cigarettes. They are expanding with cinemas and
other places where age verification is necessary. As mentioned, the software can
be useful for immigration departments. Scientific research could certainly bene-
fit from such database to study the ageing process in various ethnic groups. The
dilemma here is how to create and store large scale biometrics data allowing
a sensible secondary use of the information.
3. Big data and scientific research friend or foe
As indicated above, scientific research benefits from large scale data from
various sources. This has been made easier and more cost effective thanks to
digitalisation of information and cheap data storage. Big Data is defined by

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Zulkarnain and Anshari (2016) as “datasets whose size is beyond the ability of
typical database software tools to capture, store, manage, and analyze”. In
Kaislere, Armour and Espinosa (2016), “Big data is data too big to be handled
and analyzed bytraditional database protocols such as SQL”. Big Data are par-
ticularly large, complex datasets with high analytical potentials that automated
processing can analyse at higher speed than traditional processing.
“Big data analytics in medicine and healthcare allows analysis of the large
datasets from thousands of patients, identifying clusters and correlation between
datasets.Moreover, improving predictive models using data mining techniques”.
Big Data has large potentials helping medical research to create new growth
opportunities including in predictive healthcare. This is not without posing si-
gnificant challenges such as the loss of privacy and confidentiality. Privacy and
security are key concerns for individuals and all corporations involved.
However, open access to health data is beneficial to scientific research. We
will further discuss the European initiatives. The following sections will draw
data protection lessons from several European initiatives.
4. The requirement of quality data an essential parameter to integrate
Data accuracy is paramount for quality research results. Human or algorithm
errors/biases or sometimes technology outcomes can alter results. This issue has
been pointed out by the European Union Agency for Fundamental Rights (FRA,
2020) that raised serious concerns about the quality of medical data and the re-
sulting risk of medical errors. “The quality of data in EMR/EHR also raises
some concern. Studies where patients were shown their medical files and asked
about their accuracy found that up to 50 % of information was incomplete or
erroneous. Too many important data in EMR/EHR is unstructured in the form of
free text, which further reduces data quality”. See also Miller (2019).
In order to be validly processed, row medical data requires to undergo initial
“cleansing” (Stöger, Schneeberger, Kieseberg, Holzinger, 2021). In this paper,
the authors take “an interdisciplinary look at some of the technical and legal
challenges of data cleansing against the background of the European medical
device law, with the key message that technical and legal aspects must always be
considered together in such a sensitive context”.
These authors initially enumerate the typical data quality issues they suggest
the cleansing operation should tackle: absence of data, dummy/default, noise
(a.k.a. the “butterfly effect”, wrong data, inconsistent data, cryptic data, dupli-
cate primary keys, non-unique identifiers, multipurpose fields, violation of
(business) rules.
With reference to the ECJ and the German Supreme Court (Bundesge-
richtshof) there are potential legal consequences for faulty data in medical AI,
engaging the liability of the notified body towards third persons like patients in

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case that an assessment procedure has been carried out without sufficient dili-
gence (Case C-219/15 Elisabeth Schmitt v TÜV Rheinland LGA Products GmbH
ECLI:EU:C:2017:128; BGH 17 February 2020, VII ZR 151/18).
The authors then suggest five necessary steps to use the raw data and transforms
it into information that can be worked with in the subsequent analytical steps:
Parsing, Correcting, Standardizing, Matching, Consolidating.
Without going into further detail into these techniques, they suggest applying
standardisation and corrections to data sets sometimes from unstructured infor-
mation like manual diagnosis data to then compare and match to avoid duplica-
tion to finally solve inconsistencies. The quality of this cleansing process will
have legal consequences as they point that any damage caused by malfunctions
or other service provider due to these “faulty” data, will create a liability for the
manufacturer of the medical device mostly under the Medical Devices Regula-
tion (MDR). It is required that the product in question “shall be safe and effec-
tive and shall not compromise the clinical condition or the safety of patients”.
GDPR data accuracy requirement will not apply to the training data as data is
mostly anonymised.
This has led the European Parliament to propose that developers should,
where feasible, implement quality checks of the external sources of data and
should put oversight mechanisms in place regarding their collection, storage,
processing, and use of data (Art. 17 para. 3 European Parliament, Framework of
Ethical Aspects of Artificial Intelligence – n 46).
Also, in its report on the safety and liability implications of Artificial Intelli-
gence, the Internet of Things and Robotics, the European Commission has noted
that Union product safety legislation now “does not explicitly address the risks
to safety derived from faulty data. However, according to the ‘use’ of the pro-
duct, producers [in terms of the MDR, manufacturers] should anticipate during
the design and testing phases the data accuracy and its relevance for safety fun-
ctions” (Commission, ‘Report on the Safety and Liability Implications of Artifi-
cial Intelligence’ – n 18). The Report and the Whitepaper also call for product
safety legislation to “provide for specific requirements addressing the risks to
safety of faulty data at the design stage as well as mechanisms to ensure that
quality of data is maintained throughout the use of the AI products and systems”
(Commission, COM (2020) 65 final – n 3 – 15).
5. When Clinical Trials Go Digital, Ethics Is Needed
The pandemic accelerated the digitalisation of clinical research and wide-
spread decentralised clinical trials have brought many improvements while pri-
vacy and ethics challenges took a back seat.
In June 2021, the World Health Organization published a guidance on Ethics
& Governance of Artificial Intelligence for Health (Internet 5) outlining six key

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principles for the ethical use of artificial intelligence in health. The 20 leading
experts in ethics, digital technology, law, human rights, as well as experts from
Ministries of Healthrecommended the technology must put ethics and human
rights at the heart of its design, deployment, and use.
Artificial intelligence’s potentials in healthcare are undeniable: from AI medi-
cal imaging, scanning patient health records to predict illness, monitoring devices
to systems that help track disease outbreaks. Accessing medical speciality consul-
tations to help evaluate symptoms in remote areas. Just to mention a few (Wets-
man, 2019). The outbreak of the COVID pandemic also saw the surge of techno-
logical tools that were revealed to be controversial if not unnecessary tracking.
The Bluetooth technology widely used was inappropriate to detect the contact
tracing. In Singapore, the government admitted that the contact tracing application
collected data about health that was repurposed beyond the original goal.
Additionally, some databases were hacked, and medical data were accessed
by unlawful criminals (Internet 6). A report by WHO Ethics and governance of
artificial intelligence for health warned about AI tools developed by private
technology companies (like Google and Chinese company Tencent) that have
large resources but not always the necessary ethical incentives. Their focus may
be toward profit, rather than the public good. “While these companies may offer
innovative approaches, there is concern that they might eventually exercise too
much power in relation to governments, providers and patients” (Internet 7).
The report recommended six ethical principles: protect autonomy, promote
human safety, ensure transparency, foster accountability, ensure equity, and
promote AI that is sustainable.
In parallel, the new European regulation on AI, the AI Act (Internet 8)
which could come into effect in late 2024 is a proposed European law on artifi-
cial intelligence (AI) – the first law on AI by a major regulator anywhere. The
law assigns applications of AI to three risk categories (Internet 9). The Artificial
Intelligence in Healthcare report (Internet 10) supports the European Commis-
sion in identifying and addressing any issues that might be hindering the wider
adoption of AI technologies in the healthcare sector. The study has highlighted
six categories where the European Commission is suggested to focus to support
the development and adoption of AI technologies in the healthcare sector across
the EU. These include:
1) a policy and legal framework supporting the further development and
adoption of AI aimed at the healthcare sector in particular;
2) initiatives supporting further investment in the area;
3) actions and initiatives that will enable the access, use and exchange of
healthcare data with a view to using AI;
4) initiatives to upskill healthcare professionals and to educate AI develo-
pers on current clinical practices and needs;

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5) actions addressing culture issues and building trust in the use of AI in the
healthcare sector;
6) policies supporting the translation of research into clinical practice.
The GDPR allows for code of conduct to be approved by national superviso-
ry authorities. These can strengthen clinical research and pharmacovigilance.
The Spanish Code of Conduct (Internet 11)
A first national Code of Conduct was promoted by Farmaindustria in Spain
regulating the processing of personal data in the field of clinical trials and other
clinical research and pharmacovigilance. The code of conduct, regulates how the
promoters of clinical studies with medicines and the CROs that decide to adhere
thereto must apply the data protection regulations. Data controllers and data
processors that adhere to the code of conduct are obliged to comply with its pro-
visions.
The UK Code of conduct for data-driven health and care tech
Following a consultation, the UK Government has published a code of con-
duct for data-driven health and care technology to enable the development and
adoption of safe, ethical and effective data-driven health and care technologies:
“We have some truly remarkable data-driven innovations, apps, clinical decision
support tools supported by intelligent algorithms, and the widespread adoption
of electronic health records. In parallel, we are seeing advancements in techno-
logy and in particular artificial intelligence techniques” (Internet 12).
The Government has set out the behaviours expected from those developing,
deploying and using data-driven technologies in the health and care systems:
1. Understand users, their needs and the context.
2. Define the outcome and how the technology will contribute to it.
3. Use data that is in line with appropriate guidelines for the purpose for
which it is being used.
4. Be fair, transparent and accountable about what data is being used.
5. Make use of open standards.
6. Be transparent about the limitations of the data used and algorithms de-
ployed.
7. Show what type of algorithm is being developed or deployed, the ethical
examination of how the data is used, how its performance will be validated and
how it will be integrated into health and care provision.
8. Generate evidence of effectiveness for the intended use and value for
money.
9. Make security integral to the design.
10. Define the commercial strategy and consider only entering into commer-
cial terms in which the benefits of the partnerships between technology compa-
nies and health and care providers are shared fairly.

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6. The case of the French Health Data Hub and the COVID research
The French government has contracted the processing of health data with
Microsoft, a US corporation. Microsoft is said to have the processing capabilities
currently no other European company could have. Scientific research needs data
on COVID patients to study the spread of the virus, the scale of long term
COVID, the category of the population mostly affected or the effects of the va-
rious vaccines administered.
Several legal limitations had to be considered. First the question of the tran-
sfer of European data outside the European Union. United States being deemed
a country of non-adequate data protection since the invalidation of the Privacy
Shield agreement allowing the flow of data between the two continents. The
French data Protection Supervisory authority, The Commission Nationale In-
formatique et Libertés (CNIL) published an opinion expressing their concerns.
They recommended this measure to be only temporary, requiring further guaran-
tees from Microsoft. The French Health Minister requested the data to be stored
in data servers located within the EU. A measure that has limited protection as
regardless of the localisation of the data, US NSA Section 702, The US Cloud
Act and the Executive Order 12333 would apply.
If these are technical necessities to comply with the General Data Protection
Regulation, basically requiring the same level of data protection wherever data
travels, sharing data with a foreign company represents further challenges.
This is a consequence of the loss of data sovereignty that the European
countries are facing. Dependence on foreign corporations for processing health
data is by itself an issue. The quasi-monopolistic position of these corporations
gives them excessive power of control over data. Data is processed, possibly
monetised. The security of the data can be compromised as any database created
is data at risk.
The GDPR has several principles applying to lawful data processing, from
data minimisation, access limitation or ensuring data integrity. Big data analytics
or algorithms are by essence thirsty for data. Scientific research benefits from
the largest data sets. How to conciliate the legitimate needs of research with data
protection principles?
7. Technical means of protecting medical data: encryption, anonymisa-
tion or pseudonymisation
With data becoming increasingly digitalised and widely accessible, securing
health data is paramount. The first supplementary measure to secure data when
transferred abroad is to apply a strong encryption, keeping the encryption key
out of the reach of the US internet communication services. This is in fact diffi-
cult to realise when data needs further processing.Cybersecurity attacks and
ransomware against hospitals are becoming a common threat. Health data have

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high value and are regularly targeted by cyber criminals. Cyberattacks in the
healthcare environment have tripled since 2018 to reach 45 million individual
victims in 2021.
Medical data is collected from various sources:
- imaging techniques for diagnosis;
- electronic health records;
- robotics in surgical procedures;
- telehealth for efficiency or reaching patients in more remote locations;
- wearables to monitor individuals’ health with various Internet connected
devices with often weak security.
The use of open data sources is also instrumental in the field of genomics,
where data related to genetic makeup, biomarkers and bioinformatics is used to
derive better therapeutic solutions.
European healthcare requires stronger protection and security measures to
protect health data.
Pseudonymised personal data are data where identifiers such as names are
replaced by codes the research institutions keep. A ‘code key’ that is the link to
the individual person is kept separately from the research data in order to protect
the privacy of patients. Only fully anonymised data escape from the General
Data Protection Regulation (GDPR) requirements. In the case of data for re-
search, full anonymisation might render the data useless. Therefore, it is often
not a possible option.
Pseudonymised data and even anonymised data are not exempt from re-
identification. Even when name, date of birth or national security numbers are
“anonymised”, a full health history will reveal patients’ age, gender, the places
where they have lived, their family relationships and aspects of their lifestyle.
Privacy-enhancing technologies such as homomorphic encryption, differen-
tial privacy, federated analyses and use of synthetic data offer new ways for
protecting the privacy of individuals. New promises are seen in synthetic data
(Internet 13), still not exempt of criticism (Chen, Lu, Chen, Williamson,
Mahmood, 2021). Issues of anonymisation and de-identification need to be ad-
dressed and appropriately managed (Internet 14).
Health data being more sensitive, requires extra layers of protection and ap-
propriate security measures such as encryption. The GDPR applicability to the
data set has implications in the free flow of data to countries outside the Europe-
an Economic Area (EEA. This is a problem, for example, with researchers at
federal research institutions in the United States. Transfers to international or-
ganisations such as the World Health Organization are similarly affected (Inter-
net 15). The European scientific academies have recently published a report
explaining the consequences of stalled data transfers and pushing for responsible
solutions. (The European Academies Science Advisory Council, the Federation

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of European Academies of Medicine & the European Federation of Academies
of Sciences and Humanities (2021) (Internet 16; Bentzen et al., 2021).
The case of the Canadian PHIPA experience
Occasionally, there are privacy investigation decisions that stand out be-
cause of their precedent setting nature (Internet 17). One example is the PHIPA
Decision 175 (Internet 18) which details an investigation into the sale of de-
identified data by a health information entity to a third-party corporation. The
data protection supervisory became aware of the situation through a news article
and launched an investigation under the Personal Health Information Protection
Act (PHIPA). It is admitted that de-identification is considered to be a use under
PHIPA.
Such use for de-identification does not usually necessitate individual con-
sents. Health information custodians, who have custody and control of personal
health information have to be transparent to clearly and explicitly inform pa-
tients about their practices in their public notice. The necessity of transparency is
emphasised. It is safe to consider de-identification to be considered data pro-
cessing under the GDPR definition. Although, patients’ consent is required in
most cases. We will now take a deeper dive into the specific situation of the
European health data and its challenges. How to reconcile the need to share data,
open free access to the database, and keep data secure.
II – The paradigm of the European data sovereignty
The last few decades, we have witnessed major developments in the fields
of internet communication and data digitalisation. Europe has dragged behind
US or Chinese corporations. Our dependency on US corporations for cloud sto-
rage (AWS, Microsoft, Apple iCloud, etc…) or computer operating systems
(Microsoft, Apple iOs or Google) or data analytics is undeniable. It’s with the
COVID pandemic that EU governments have had a wakeup call, realising the
complexities of creating a contact tracing app without the help of a Google App
or its ID verification Captcha. The de-centralised tracing application could not
be successful. Since the European Court of Justice decision in July 2020 invali-
dating data transfers to the US, the use of US internet communication services
became problematic. European countries are re-thinking data processing and
data storage solutions including in the fields of health data and research.
1. European health data initiatives promise to open new perspectives
for medical research
A – EU Health Data
New initiatives are underway for the EU Health data centre and common
health data to be accessed more efficiently. “Early lessons learnt with COVID-19

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have shown that the current system has not ensured an optimal response at EU
level to the COVID-19 pandemic”.
“Regarding health data, its availability and comparability, the Covid-19
pandemic revealed that the EU has no clear health data architecture. The lack of
harmonisation in these practices and the absence of an EU-level centre for data
analysis and use to support a better response to public health crises is the focus
of this study. Through extensive desk review, interviews with key actors, and
enquiry into experiences from outside the EU/EEA area, this study highlights
that the EU must have the capacity to use data very effectively in order to make
data-supported public health policy proposals and inform political decisions. The
possible functions and characteristics of an EU health data centre are outlined.
The centre can only fulfil its mandate if it has the power and competency to in-
fluence Member State public-health-relevant data ecosystems and institutionally
link with their national level actors. The institutional structure, its possible acti-
vities and in particular its usage of advanced technologies such as AI are exami-
ned in detail”.
Study by Henrique Martins of ISCTE-Lisbon University Institute and Facul-
ty of Medical Sciences, UBI Portugal, was made public at the request of the
Panel for the Future of Science and Technology (STOA) and managed by the
Scientific Foresight Unit, within the Directorate-General for Parliamentary Re-
search Services (EPRS) of the Secretariat of the European Parliament is stagge-
ring (Internet 19).
Sadly, the finding of this study is striking : the absence of harmonisation de-
spite new initiatives such as the ‘European Health Union’ umbrella, or the EC
proposal for the creation of a new European Health Emergency. “There is NO
comprehensive health data governance at the EU level, and very few MS [Mem-
ber States] could be said to have one at the national level as well while a centra-
lised governance structure is needed to deal with large scale data for preventive
and curative actions”.
The suggested model could be Preparedness and Response Authority (HERA)
(Internet 20). On 3 May 2022, the Commission published the Proposal for a Re-
gulation of the European Parliament and of the Council on the European Health
Data Space Act (“the Proposal”).
European Data Protection Board (EDPB) and the European Data Pro-
tection Supervisory (EDPS) were required to give their opinions on this
project (Internet 21)
Since the European Data Strategy for the creation of a single European Data
Space for primary use of medical data, all EU citizens will have access to their
electronic health records by 2030 thanks to the EU’s central eHealth platform
linking national contact points to the MyHealth@EU infrastructure and efficient
national digital health authorities.

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B – Joint Action Towards the European Health Data Space – TEHDAS
The European Health Data Space (EHDS) could enormously impact health
research if it can overcome barriers to cross-border secondary use of health data
and create trust amongst citizens, according to a Finnish health data stakeholder
(Internet 22). The TEHDAS project, based on the European Commission’s
Health Programme 2020, develops European principles for the secondary use of
health data from early 2020. Carried out by 25 European countries and co-
ordinated by the Finnish Innovation Fund, Sitra (Internet 23).
European health projects will ease the share of data flow, with standardisa-
tion and the creation of a central authority. Free access to this data will benefit
innovation at the European and international level.
III. Access to data for research
Re-using data and open science:
1- International cooperation
1.1 – Within the National and international initiatives supporting medical
data health is the International Medical Informatics Association (IMIA) Open
Source Working Group (OSWG) (Internet 24), “a voluntary group supported by
IMIA that brings together researchers and practitioners from multiple countries
with a diverse range of informatics experience but common interest in the adop-
tion of open approaches to advancing the use of informatics to improve
healthcare”. This has led to the development of an open access database of Free,
Libre, and Open-Source Software (FLOSS), called MedFLOSS (Internet 25)
(www.medfloss.org), to apply in the medical domain to accelerate medical re-
search.
1.2 – Harmony Alliancepromise to accelerate scientific research in
Haematology
HARMONY claims to be the Next-generation science, sharing data and
knowledge “To accelerate scientific research on Hematologic Malignancies
(HMs), broad cooperation between all healthcare stakeholders is urgently needed.
Each single piece of the puzzle, namely each set of data, might turn out to be the
missing piece that will make it possible to treat the disease faster and better.
The HARMONY Alliance (Internet 26) has created a transparent and secure
repository for data from various clinical studies. Everybody can contribute and
help fight HMs”.
“Sharing data is crucial to further research in Hematological Malignancies
because many of these cancers are heterogeneous in terms of their genetic profile.
Moreover, many genetic profiles are rare; many account for < 10% cases. In order
to ascertain the prognostic or predictive value of genetic biomarkers, researchers
need to study large cohorts of patients. This is only achievable by sharing da-
ta,” says Anthony Moorman, Newcastle University, one of the HARMONY Key

140
Opinion Leaders for Childhood Leukemia and Project Leader of the HARMONY
Research Project entitled ‘Use of Big Data to improve outcomes for patients
with Acute Lymphoblastic Leukemia (ALL) (Internet 27).
A – Call to Remove obstacles to sharing health data with researchers
outside of the European Union (Internet 28)
Scientific academies in Europe (the European Academies Science Advisory
Council, the Federation of European Academies of Medicine, and the European
Federation of Academies of Sciences and Humanities) [The European Acade-
mies Science Advisory Council, the Federation of European Academies of Medi-
cine & the European Federation of Academies of Sciences and Humanities (In-
ternet 29) have joined forces to call attention to the challenges that affect not
only European scientists but collaborators worldwide.
In this paper (Internet 30), the co-authors,Bentzen et al. develop the necessi-
ty to levy data sharing barriers: “COVID-19 has shown that international colla-
borations and global data sharing are essential for health research, but legal ob-
stacles are preventing data sharing for non–pandemic-related research among
public researchers across the world, with potentially damaging effects for citi-
zens and patients. International sharing of pseudonymized personal data among
researchers is key to the advancement of health research and is an essential pre-
requisite for studies ofrare diseases or subgroups of common diseases to obtain
adequate statistical power”.
Certainly, the way forward requires a move from the US. “The United States
should be encouraged to establish enforceable data subject rights and effective
legal remedies for European and other non-US research participants whose data
are processed by US researchers. The voice of the health-research community
must be heard by decision-makers at the national level, at the EDPB, and within
the EU Commission Directorates-General involved, such as in the areas of jus-
tice, health and research. Without a quick resolution, European research poten-
tial will not be realized, and European citizens will fall behind”.
We saw the importance for EU research to benefit from EU health data.
Currently, European health data is targeted by US corporations. A situation that
has been criticised.
B – GAFAM US Corporations access to EU health data
1 – The case of the NHS data agreement with DeepMind before Palantir
In this paper, The Privacy and Security Implications of Open Data in
Healthcare A Contribution from the IMIA Open Source Working Group, the
authors, Shinji Kobayashi, Thomas B. Kane and Chris Paton, raise the issue of
data anonymisation, and other ethical, and governance challenges (George,

141
Whitehouse, Duquenoy, 2013; Hopia, Punna, Laitinen, Latvala, 2015; Menviel-
le, Audrain, Menvielle, 2017).
The paper reveals how in 2003, NHS England launched care.data in order to
combine all healthcare records stored by general practitioners with all infor-
mation stored by social services and hospitals. All data was held into the natio-
nal Health and Social Care Information Centre (HSCIC) databases.
The Hospital Episode Statistics dataset on the other hand collects and curates
data from 125 million individuals in England every year. If this data set can have
huge potentials for research, it raises the methodology of conservation questions.
UK Care.data has been criticised for sharing data with pharmaceutical com-
panies, insurance companies, health charities, hospital trusts, think tanks, and
other private companies. In 2014, it was disclosed that anonymous, pseudony-
mous, and identifiable data was sold to 160 organisations (Bahatia, 2014). In
response to a Freedom of Information request, HSCIC stated: “We recognise
that there will however remain a latent risk that when combined with other
sources of data, the identity of the individual may be ascertained”. Care.data was
closed in 2016 following general criticism and opt out.
In June 2017, Taunton and Somerset NHS Foundation Trust and DeepMind
Healthcare signed a 5-year contract to develop and evaluate a system able to
detect early signs of kidney-failure (Internet 31). Over 1.6 million live NHS data
records were given to Google, via DeepMind.
In November 2017, the UK Information Commissioner ruled that the Lon-
don’s Royal Free hospital failed to comply with the Data Protection Act when it
handed over personal data of 1.6 million patients to DeepMind (Internet 32).
A DeepMind spokesperson said the firm “underestimated the complexity of the
NHS and of the rules around patient data”. According to the ICO, Elizabeth
Denham, their “investigation found a number of shortcomings in the way patient
records were shared for this trial. Patients would not have reasonably expected
their information to have been used in this way, and the Trust could and should
have been far more transparent with patients as to what was happening”. The ICO
warned that such work should never be “a choice between privacy or innovation”.
Despite privacy advocates’ hopes, this ruling did not exclude the use of the
app. ‘Streams’ that has since been rolled out to other British hospitals, and
DeepMind has also branched out into other clinical trials, including a project
aimed at using machine-learning techniques to improve diagnosis of diabetic
retinopathy (Internet 33), and another aimed at using similar techniques to
better prepare radiotherapists for treating head and neck cancers.
Google’s DeepMind Health systems have potential benefits for patients,
nurses, and doctors (Internet 34). The DeepMind ‘Streams’ app allows clinicians
to be informed when patient vital signs deteriorate using data from patient-
monitoring technology that deliver in real-time significant patient life-sign indi-

142
cators to the clinician’s mobile device. If the medical potentials have major be-
nefits, they raise data ownership, secondary usage and ethics concerns.
Julia Powel, from the Faculty of Law and Computer Laboratory, University
of Cambridge, was one of the early scholars warning about the ethical issues of
Stream App (Internet 35) sharing NHS data with Google DeepMind.
Google DeepMind and healthcare in an age of algorithms (Internet 36)
“Data-driven tools and techniques, particularly machine learning methods
that underpin artificial intelligence, offer promise in improving healthcare sys-
tems and services. One of the companies aspiring to pioneer these advances is
DeepMind Technologies Limited, a wholly-owned subsidiary of the Google
conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health
project: a collaboration with the Royal Free London NHS Foundation Trust, to
assist in the management of acute kidney injury. Initially received with great
enthusiasm, the collaboration has suffered from a lack of clarity and openness,
with issues of privacy and power emerging as potent challenges as the project
has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this arti-
cle draws a number of lessons on the transfer of population-derived datasets to
large private prospectors, identifying critical questions for policy-makers, indu-
stry and individuals as healthcare moves into an algorithmic age”.
In a more recent episode, the NHS body was again criticised for being re-
sponsible for delivering IT strategy that struggled to ensure patients understand
that medical data held by their GPs will be copied into a central database to be
shared with third parties unless they opt out by 23rd of June 2022. Conflicting
messaging overshadows NHS Digital’s attempts to inform the public about pa-
tient data slurp (Internet 37).
When a new UK project came up, as reported by The Guardian (Internet
38), “more than a million people opted out of NHS data-sharing in one month
in a huge backlash against government plans to make patient data available to
private companies, the Observer can reveal. The General Practice Data for Plan-
ning and Research scheme is now on hold with no new date for implementation,
and NHS (Internet 39). Digital has made a series of concessions to campaigners
to try to salvage it”.
The Guardian published an article by EerkeBoiten titled Our personal
health history is too valuable to be harvested by the tech giants (Internet 40).
In December 2019 The Observer revealed (Internet 41) how UK medical da-
ta was allegedly sold to American drug companies with little transparency or
accountability around the process. “US drugs giants, including Merck (referred
to outside the US and Canada as MSD, Merck Sharp and Dohme), Bristol-Myers
Squibb and Eli Lilly, have paid the Department of Health and Social Care, which
holds data derived from GPs’ surgeries, for licences costing up to £330,000 each
in return for anonymised data to be used for research”.

143
These revelations raise big questions over the transparency and claims of
anonymity in NHS data transfers through the research scheme used by the
health service. It appears that individual-level. NHS Digital has announced GP
medical records in England would be collected via a new service called Ge-
neral Practice Data for Planning and Research (GPDPR) (Interned 42). It will
replace the General Practice Extraction Service (GPES), which has operated
for over 10 years.
More recently, it was revealed that Palantir awarded £23m deal to continue
work on NHS Covid-19 Data Store (Internet 43) “The two-year contract was
first reported today by (Internet 44) who have campaigned for transparency sur-
rounding deals between the NHS and big tech firms”. openDemocracy and Fox-
glove claim the contract was “secretly” signed “in apparent violation of their
[the government’s] prior promise to conduct future contracts between the NHS
and big tech via a full and open public tender”.
Medical data harvesting in France
The US Palantir’s projects are not limited to the UK. It has been reported
that US data giant Palantir is on a mission to seduce France’s start-ups”. Fears
might not be unfounded as Palantir is said to be one of the most secretive com-
panies in the world. Palantir (Internet 45) has expertise in big data analytics hav-
ing initially worked for the US armed forces and intelligence services.
More recently, French investigative journalists (Internet 46) revealed how
medical data was sold including drugs sold by pharmacists to Iqvia (Internet 47).
The group had been tracking each patient via a unique identifier number to carry
out “analyses of sales of health products aggregated by typologies of pharma-
cies, by main types of prescribers and by geographical areas”.
Following the broadcast of the program, the French data protection authori-
ty, CNIL, referred the case to the Paris Judicial Court (Internet 48) asking Inter-
net Service Providers (ISPs) to block access to a site hosting health data of near-
ly 500,000 people. The CNIL, which has already carried out three controls on
this data leak, is continuing its investigations.
In April 2022, the French CNIL fined Dedalus 15 million Euros for their
lack of security measures having led to the breach of medical data of 500.000
individuals online (Internet 49).
AMAZON, US online retailer coming big in the health data market
medical data
The acquisition of One Medical gives Amazon access to more data. One
Medical built its own electronic medical records system, and it has 15 years’
worth of medical and health-system data that Amazon could tap.

144
In the US, it is reported that Amazon Pharmacy (Internet 50) has partnered
with Blue Cross Blue Shield plans in five states and pharmacy benefit manager
Prime Therapeutics to offer a prescription discount savings card (Internet 51).
Amazon’s access to these medical data has to be considered in the context of
the mass of various data the corporation has access to (Internet 52).
Medical data have high value. They are naturally targeted by big pharma-
ceutical companies.
As increasingly pseudonymised or even anonymised data have the potential
to be re-identified, the share of such sensitive data is not without controversy.
Article 29 working party (Internet 53) has specified that “to identify if a person
is identifiable, account should be taken of all the means likely reasonably to be
used either by the controller or by any other person to identify the said person”.
This is at the centre of the question of patients’ data security. Patients might
have personal reasons to remain anonymous therefore it is paramount the securi-
ty and confidentiality of often sensitive medical data to be respected.
Conclusion
A balance must be struck between protecting individual patients’ fundamen-
tal rights of privacy and dignity, the need for research to access data, and for the
industry to study the impact of medical data on health improvement. Applying
privacy by design, privacy enhancing technologies, anonymisation and encryp-
tion in full transparency and respect of individual needs and wishes is para-
mount. Health data has huge value. “Regulators” vigilance to monitor data han-
dling is essential. EU health data sovereignty will not only improve the privacy
of patients and protection of their data, it will also impact the European research
capabilities.
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