04 Nov 2020

Improving lives and managing diseases through a data driven EU healthcare system

DIGITALEUROPE welcomes the European Commission’s efforts to establish a Common European Health Data Space (EHDS). In this paper, we outline our key recommendations to unlock the potential of health data in the EU and create more resilient healthcare systems.

Listen to Director for Digital Transformation Ray Pinto’s summary of our policy paper

Executive summary

Data-fuelled technologies will lead us to a society where better disease prevention, personalised medicines and faster, more accurate diagnoses and therapy become possible in more efficient care processes. The EU needs to leverage them fully across all Member States. COVID-19 has demonstrated that digital technologies can play an important role to enable health workers in combating the virus, ensure remote care for immunodeficient patients, keep communities informed and empowered, support health population management and accelerate research on treatments, vaccines and cures.

Ultimately, our goal is to accelerate data flows within the EU for better health outcomes for everyone. Health data makes up 30% of the world’s stored data.[1] A single patient generates up to 80 megabytes yearly in imaging and EMR data.[2] However, such valuable data is often inaccessible, even to the patient, and nationally siloed and shielded. Such non-technology barriers from governments have slowed down the Electronic Health Record (EHR) adoption to 3% in Europe compared to the United States which is 35%.[3] Understandably, health data is sensitive, but unlocking this wealth of information based on trust and legal certainty can save and improve lives. For instance, a study has shown that AI – which is fuelled by reliable and secure data – can extend average life expectancy by 0.2 -1.3 years.[4]

Data driven health innovations are key to saving more lives, which has become ever clearer during the pandemic. Besides the tragic damages from the COVID-19 virus, the disruption this has caused led to unfortunate, yet preventable losses in much needed care everywhere. During the first half of 2020, when the pandemic directly caused half a million deaths in Europe, the block counted 2.7 million new cancer patients – while 1.3 million lost their lives.[5] The pandemic challenged 88% of caregivers in providing care and more than half had to reduce their services.[6] Non communicable diseases patients did not have access to the treatment they needed; Disruptions mounted up to 49% for treatment for diabetes, 42% for cancer treatment, and 31% for cardiovascular emergencies. These disruptions are preventable. Part of the cause was the lack of data, diagnostics and other technologies.[7]

A truly connected, interoperable and sustainable Common European Health Data Space is a precondition to unlock the potential of health data in the EU. It will ensure that Europe’s clinical research and treatments will pivot our society towards value-based healthcare models and systems. For that to happen, the EU should focus on three main areas:

  • A framework of trust and legal clarity
    • Harmonise the mechanisms by which personal health information can be shared (e.g. a common approach to pseudonymisation and/or anonymisation) in the EU.
    • Establish a consistent harmonised model for a central health data authority in each Member State to facilitate the processing of the secondary use of health data for both the private and public research institutions.
    • Build on responsible data sharing initiatives driven by industry, like YODA,[8] and guarantee private sector participation in the Data Space, while safeguarding Intellectual Property Rights.
  • Interoperability and standardisation
    • Advance federated data models.
    • Foster convergence and acceleration of deployment of Health IT (HIT) interoperability standards such as Fast Healthcare Interoperability Resources (FHIR) building on the Commission Recommendation on a European Electronic Health Record exchange format.[9]
    • Define a common EU health data classification to help organisations categorise identifiable, anonymised and pseudonymised data.
    • Confirm appropriate encryption tools and security standards that should be used to process sensitive health data.
  • Increase the potential of digital through investments and ambition
    • Use Next Generation (Recovery and Resilience Facility) funds and the next Multi-Annual Financial Framework (MFF) to radically upgrade the digital capabilities of health systems, including cloud technology, as a secure and economic infrastructure for driving digital transformation.
    • Fulfil the ambition and scope lined out in the Commission Recommendation on a European Electronic Health Record exchange format.

1. A framework of trust and legal clarity

Trust remains the bedrock to build the Common European Health Data Space. Patients, consumers, healthcare professionals and society will unleash the potential of health data only if there is a clear and comprehensive framework of trust and clarity on how health data should be shared consistently across Member States. Addressing the following aspects is crucial for this framework of trust:

1.1  Privacy

A Common European Health Data Space requires a harmonised framework of health data privacy in Europe. The GDPR made possible the sharing and cross-border flow of health data by establishing the foundations of a trust framework for patients, consumers and other stakeholders. But its interpretation and implementation still diverge among Member States. COVID-19 exacerbated the negative impact from this fragmentation.[10] We need decisive EU action to harmonise conditions for health-data processing for primary and secondary use across Europe. We urge to:

  • Create an EU Code of Conduct (CoC) on the processing of genetic, biometric, or health data. The CoC should accelerate the access and processing of such data within each, and across all, Member States in cooperation with all key public and private European cooperation to fight diseases and viruses, population health management at scale and support to safe cross-border travel are concrete examples of why we need a CoC. It must entail:
    • Public interest as legal basis for circumstances in Article 9.2 of the GDPR. The CoC should also give a common interpretation of what is considered “public interest” by national authorities across the EU. Unjustified, restrictive Member States’ interpretations of public interest are preventing hospitals from sharing life-saving data with relevant organisations.
    • A consistent legal interpretation of ‘personal data’. The value of data lies in its use and re-use, which strongly depend on the nature of the data involved (personal data vs non-personal data). Personal data falls under the GDPR and its processing is subject to numerous data protection legal restrictions, which do not apply to non-personal data. Member States, however, do not hold a unique and aligned position on the legal concept of personal and non-personal data. No adequate and recognised standards exist on the anonymisation of personal (health) data. The CoC should fill this gap.
    • A consistent anonymisation model that provides traceability back to the source records without representing a risk for subject identification, using the concept of k-anonimity based on existing international best practice standards. It would facilitate data sharing from institutions to researchers, between pharmaceutical companies (for example to limit the need for a placebo/standard-of-care arm in a clinical trial) as well as from pharmaceutical companies to government-funded research initiatives. Data Protection Authorities are adopting excessively strict and divergent interpretations of what constitutes anonymous or anonymised data. This hinders health data processing and makes very difficult for entities to agree on whether and how parties can use the data at issue.
    • An opt-out model for secondary use of data in research fields with higher patient identification sensitivities. This model would suit areas like rare diseases, genomes and personalised medicine, with higher re-identification risks than normal and where complete anonymisation may impact the successful research outcome. A robust ethical and security framework with a strong transparency dimension would build necessary patient trust in this model and guarantee that vital identifiable data for research progress is handled properly. It would entail patient rights to actively object to their data being processed.
    • Practical guidelines which can support practitioners along the healthcare value chain (including patients, physicians, healthcare managers, industry). They should provide a common data classification framework,[11] providing clarity on how identifiable, anonymised and pseudonymised data should be categorised and where it can be stored and processed.
    • A reduction of fragmentation of local conditions on data processing for scientific research purposes. The processing of health data for scientific research purposes is authorized by Art. 9 (j) of the GDPR. Yet, Art. 9 (4) of the GDPR allows Members states to introduce further conditions and limitations to the processing of health data. This provision has resulted into the introduction of country- and region-specific constraints to the processing of health data for scientific research purposes, such as those around the concept of “public interest of the research”, the “impossibility or disproportionate effort to obtain consent” and the concept of “research institute or body”. The resulting patchwork of different rules across the EU is hindering health research. The CoC should create consistency on the use of health data for scientific research purposes and pave the way for a harmonisation of the local implementation of the GDPR.
  • Issue European Data Protection Board (EDPB) essential guidance on the GDPR in collaboration with industry, the European Medicines Agency (EMA) and relevant national authorities. It is fundamental to bring harmonisation on:
    • the concept of personal data
    • the use of public interest, scientific or historical research purposes, legitimate interest and consent as legal basis
    • the compatibility of primary and secondary use of data
    • the interaction between the GDPR and local and national regulations affecting health data processing
    • the use of Real-World Data (RWD) for medicine discovery and development. Data collected in real life settings[12] can help drive new understandings of value.

1.2 Data infrastructure

The exponential proliferation of data has the potential to transform healthcare and deliver unprecedented levels of quality and efficiency of care. Although multiple initiatives exist across Europe, we observe a lack of coordination and scale, as well as a fragmentation of resources and funding and an abundance of legal and privacy-related boundaries. The Common European Health Data Space requires a robust, secure and interoperable infrastructure with a clear governance framework and defined services. This is key to unlock the potential of health data in Europe.

A well-defined, common data infrastructure is fundamental to facilitate a consistent and secure secondary use of health data. The EU should therefore establish a central health data entity at EU level to select standards and profiles for interoperability, as well as a health data entity in each Member State to implement those standards. The role of the national entities should be to provide controlled data services, like healthcare information sharing and analysis. FinData in Finland and France’s Health Data Hub could inspire the creation of the entities in each Member State.

The Commission should use the planned legislative governance framework for the European Health Data Space to create this infrastructure. It should establish the legal foundation of both the EU-level health data entity as well as the national health data entities, and mandate national health entities’ adherence to the same set of rules, standards and profiles of standards selected at EU level, and in line with FAIR principles in data sharing and access.[13]

A common, pan-European infrastructure as here suggested would help remove health data-sharing obstacles, boost the exchange of cross-border health data across the EU and guarantee health data interoperability, while optimizing scale advantages in global supply markets for healthcare IT and medical devices by building on leading, internationally developed standards and profiles.

Finally, for such governance framework to be effective, it is also crucial the Commission institutes a broad definition of what constitutes scientific research. In today’s AI and big data age, a broad range of commercial activities may qualify as scientific research.

1.3 Data altruism

We support health data “altruism” and donation schemes to give clear, easy and secure ways for citizens to give access to their health data for the public good, in compliance with the GDPR. Control over personal data should remain with the patients/citizens themselves. They should be empowered to access and manage their own health data. Policymakers have an important role in making data donation and altruism a driver for healthcare innovation. We recommend them the following:

  • For regular or continued data donation, to create GDPR-compliant, European standard forms between data donors and recipients so to establish a legal basis and a strong foundation for long-term data processing activities in areas like research.
  • For one-off donations, to develop a standard, GDPR-compliant European consent form so to make the approval process by donors quick and efficient. The form could foresee data portability requests where necessary.
  • To illustrate data donation use cases for citizen awareness and educate citizens about the benefits of data donation for their health and lives. This is crucial to convey to potential data donors why aggregated data is important to advance research and innovation for society’s benefit.
  • To encourage data altruism via model contractual clauses or data sharing agreements agreed by individuals.

1.4 Transparency and confidentiality

Data transparency contributes to the framework of trust to unlock the potential of health data in the EU. It can advance medicine knowledge and ultimately improve public health.  The biopharmaceutical and medical device industry are playing their part to advance that, by promoting clinical research data sharing that benefits researchers and, ultimately, the healthcare community at large. A relevant example is the Yale Open Data Access (YODA) [14] Project, founded to promote data sharing among the scientific community and develop to advance responsible data sharing. YODA provides increased access to anonymised pharmaceutical and medical device clinical trial data and clinical study reports provided by businesses supporting the initiative. A panel in the project independently reviews and makes final decisions on all requests from qualified researchers, physicians and investigators looking to access such data for the benefit of healthcare innovation.

We support industry-driven initiatives such as YODA which enhance transparency while respecting businesses’ rights to data confidentiality.

1.5 Ethics

The Commission, Member States and all relevant key stakeholders (industry, academia, health institutions and patients) should develop ethical principles for healthcare data generation, use, re-use, and curation. They should address security, transparency and privacy based on the Ethics guidelines for trustworthy AI developed by the European Commission AI High-Level Group.[15] These ethical principles should recognise citizens remain in control over their personal data.

1.6 Culture

Trust means also robust data understanding and awareness among officials, payers, practitioners, patients and citizens. As with all technological innovations, including data-driven ones, awareness-raising is key to build acceptance in society. Healthcare stakeholders and policymakers should take a holistic approach to digital health and data literacy. They should create a data culture that encompasses collaboration and partnership amongst healthcare practitioners, payers, patients and citizens. Policymakers should ensure that:

  • Every citizen has access to digital literacy and skills training. Digital literacy is critical to ensure citizens and patients are empowered to manage their own data and capable of taking informed decisions. This should extend not only to the use of digital health technologies, but also to the ethics, governance and advantages of using healthcare data to benefit all citizens. They should leverage key stakeholder-driven initiatives such as Data Saves Lives.[16] There are existing training programs available from industry, many of which can be provided free of charge, which Member States could access.
  • Health professionals have the necessary skills to unlock the potential of data. ICT specialists are just 1% of healthcare workforce and up to 70% of health professionals do not use digital solutions due to gaps in knowledge and skills in data analytics.[17]
    • Member States must prepare tomorrow’s healthcare talent with digital-ready university curricula. Data science and artificial intelligence should be at the centre of a major reform of education systems in Europe, supported by the EU. No health system can be resilient without digital literacy and the necessary digital skills among health professionals.
    • The update to the Digital Education Action Plan, Erasmus +, Horizon Europe and the upcoming Pact for Skills should make sure no one is left behind in the healthcare workforce and that practitioners are able to make use of innovative technologies that benefit healthcare. National authorities should design ambitious digital skills programmes tailored to the healthcare workforce.
  • Local officials become ambassadors and ethical advocates of the digitalisation of healthcare in their communities. This is crucial to raise awareness of digital health innovation at local level across the EU.

2. Interoperability and standardisation

Achieving interoperability among healthcare systems and seamlessly exchanging information and data is critical to improving clinical operations and patient outcomes. 80% of health data remains unstructured and untapped after it is created.[18] Health data siloes prevent practitioners, researchers, authorities and businesses from capturing, analysing and applying valuable information to care delivery, improvements and decisions. Fundamentally, a lack of interoperability directly impedes health systems from providing effective care to citizens, and prevents data from being shared even within health systems. The Common European Data Space must ensure data systems are interoperable and therefore data sets are exchangeable and interpretable, and citizens have control of their personal data. Patient-generated data, clinical data[19] and data from other sources should all be seamlessly accessible and uniformly interpretable through interoperability of devices and systems to unlock the value of digital in this space.

2.1 Common data models

Internationally recognised standards are a critical element to achieve a more outcome-based healthcare systems across the EU. The Commission and Member States should advance federated data models, whose goal is to analyse RWD standardised to common data models. Such models would facilitate interoperability and connectivity while respecting GDPR requirements. Their advantage lies in unlocking access to healthcare data and thus facilitating learning healthcare systems,[20] all while ensuring the highest level of protection of personal data and commercial IP. Through a federated model, the different sources of healthcare data act as nodes in a network. Importantly, the data remain on site, unaltered and uncompromised. It is only the final output of the data analysis that is shared within the framework under secure, legally compliant conditions. Actors can use it to inform research, clinical treatment, hospital planning and payment models, and influence the effectiveness of the overall healthcare demand and supply value chain. EU citizens and patients should be at the core of such a network and remain empowered throughout, so no provider can prevent them from managing or accessing their data. A key example of federated data model projects is the Innovative Medicines Initiative (IMI) initiative EHDEN,[21] which builds upon the Observational Medical Outcomes Partnership (OMOP) Common Data Model(CDM) launched in the US. The OMOP CDM standardises different structures across disparate health data sources into common tables which harmonise structure, field datatypes and conventions.

There are also other projects on common data models focusing minds of policy-makers and the health community. The EMA and the Heads of Medicines Agencies (HMA), for example, have called[22] for the establishment of DARWIN,[23] a European network of databases of known quality and content associated by a strong focus on data security. DARWIN’s role would be to extract valuable information from multiple, complimentary RWD databases to support regulatory decision-making.

2.2 European Electronic Health Record exchange format

Fulfilling the ambitions in Recommendation on a European Electronic Health Record exchange format[24] should be key for the Commission. Priority should be given to:

  • The completion by 2022 of the exchange of electronic patient summaries and ePrescriptions between various Member States.
  • Progress on the other baseline domains identified in the Recommendation. We need profiles providing specifications for interoperability also for laboratory results, medical imaging and reports, and hospital discharge reports. These information domains showed to be vital in the fight against COVID-19 across Europe. The Commission should complete these profiles before 2024 and support their practical implementation to meet clinical needs.
  • Convergence on specifications selected for data exchanges between health applications. Convergence will provide strong investment incentives for vendors to comply with prioritised specifications and develop them further. One example are the Existing Fast Healthcare Interoperability Resources (FHIR)standards. They are consistent, easy-to-implement information models used by all major cloud providers and health technology application developers. They also build on similar specifications in related ICT health solutions. Other examples are the DICOM standards and the IEEE Xplore digital library.

2.3 Common data classification framework

Europe should encourage a common health data classification framework to help organisations categorise identifiable, anonymised and pseudonymised data. This is especially important as the volume of global healthcare enterprise data is set to grow at a faster rate than the global average data volume.[25] Properly managing this growing amount of data becomes crucial as it helps organisations to consistently identify data which belongs to a special category or is potentially high risk for sharing, and take appropriate mitigating actions.

A notable focus area on data classification should be to enable the use of all classes of healthcare data with cloud technology, with appropriate levels of security and risk mitigation in place which corresponds with the type of data being processed. The use of cloud computing is growing in important scenarios like global research collaboration, predictive analytics for early disease detection and population health management. We need a more harmonised approach to utilising cloud technology for healthcare workloads. This could be enabled through a commonly adopted risk assessment framework for different types of health data. It would help data controllers to adopt cloud technologies with the appropriate architecture, security and privacy considerations, and ultimately benefit healthcare innovation. We highlight guidance from NHS Digital[26] as one among existing good practices and encourage the Commission to also explore others. NHS Digital acknowledges cloud technology benefits, including for the use of data analytics environments pooling together anonymised data, which is a similar concept to the upcoming Common European Health Data Space. Other healthcare systems across Europe, Data Protection Authorities and the European Commission should define together similar guidance at European level. In Annex I below, we detail the main elements of the NHS Digital’s Data Risk Assessment guidance to inspire policy-makers’ thinking on this issue. We also encourage how the European Open Science Cloud initiative seeks to create a more common understanding of vocabularies and semantic registries to advance data analytics tools in research, including in health.


3. Investments in digital solutions

The EU has well-tested secure digital solutions from across the globe available for it to build capacity for the Common European Health Data Space. These technologies can deliver advanced services and address areas like interoperability of health IT systems, which require ambition from Commission and Member States to fulfil the goals in the 2019 Recommendation on a European Electronic Health Record exchange format. Large volumes of rich and quality data will enable transformative technology like AI and machine learning to achieve a precise diagnosis, support personalised therapies, draw new patient- and disease-level insights and advance research in vital areas like genome sequencing. They will enable medicines to deliver on their full potential with personalised treatments, and ultimately revolutionise medical science.

We urge policymakers to:

  • Recognise Next Generation EU offers an unprecedented opportunity to transform healthcare systems. On average, health sectors in developed economies spend just 10% of total expenditure on software and databases, less than other large sectors like finance and machinery.[27] Member States’ Recovery and Resilience Plans should upgrade medical equipment, IT systems and software used in hospitals, medical centres and research labs. We need a paradigm shift in Europe from investments into legacy infrastructure to investments into future-oriented, well-tested technologies capable to project us towards the sustainable, digitalised healthcare systems we need. Data-driven, personalised care requires a strong technology infrastructure as much as a framework of trust and standardised, interoperable systems and devices.
  • Prioritise resources for setting up EHDS governance. There should be ample funding reserved in the next MFF (i.e. Health Programme) that is dedicated to the setting up of the governance institutions that will be needed for the creation and functioning of the EHDS. Monitoring and coordinating the implementation of common standardisation will demand meaningful investment. This could be realised with an ambitious Health Programme.
  • Accelerate privacy-preserving machine learning and confidential computing solutions. To run collaborative data analytics exercises that guarantee the privacy of datasets used. Techniques like homomorphic encryption minimise any risk of re-identifying anonymised patient data, allowing for AI computation directly on encrypted data.
  • Use the Digital Europe Programme (DEP) to establish health-focused, world-reference AI testing facilities. Placed across the EU, they should partner with healthcare actors to test AI solutions in real operational environments. This is key for health organisations to nurture the growth of data scientists at the forefront of healthcare innovation.

[1] EMERJ (2019). Where Healthcare’s Big Data Actually Comes From

[2] NEJM (2017). Using It or Losing It? The Case for Data Scientists Inside Health Care

[3] McKinsey (2019). Promoting an overdue digital transformation in healthcare.

[4] McKinsey Global Institute (2017). Artificial Intelligence: The Next Digital Frontier?

[5] JRC (2020). Ireland is the country with the highest cancer incidence in the EU

[6] ESMO (2020). Covid-19 Pandemic Halts Cancer Care and Damages Oncologists’ Wellbeing

[7] WHO (2020). COVID-19 significantly impacts health services for noncommunicable diseases

[8] YODA stands for Yale Open Data Access (YODA) Project. More info here

[9] Accessible here

[10] We also intend to highlight good practices:

  • The OpenSAFELY platform researched risk factors for death from COVID-19 using an unprecedented scale of Electronic Health Records from 17 million NHS patients, all in a manner compliant with both the GDPR and the UK Data Protection Act of 2018. More info here.
  • The World Health Organization (WHO) has a COVID-19 interactive map which gives a daily update on the latest global—and country-specific—numbers of COVID-19 cases. This draws on epidemiological data from around the world and relies on automatic web content extraction, data analytics, processing and storage. More info here and here.

[11] See section 2.3 for more details.

[12] RWD can include a range of routinely collected data sources from EHR, hospital databases, electronic registries and insurance claims to wearables, apps, and device-generated data, amongst others.

[13] FAIR stands for Findable, Accessible, Interoperable, Re-usable

[14] Johnson & Johnson, a DIGITALEUROPE member, is a member of YODA. It is currently making clinical trial data for pharmaceutical, medical device, and consumer products available. More info on YODA here

[15] Available here

[16] More info here

[17] OECD Health Policy Studies, Health in the 21st Century: Putting Data to Work for Stronger Health Systems, 2019

[18] Kong, Hyoun-Joong. (2019).Managing Unstructured Big Data in Healthcare System. Healthcare Informatics Research.

[19] Including high-dimensional (e.g. omics) data

[20] Healthcare systems in which knowledge generation processes are embedded in daily practice to produce continual improve in care

[21] More info here

[22] More info here

[23] DARWIN stands for Data Analysis and Real World Interrogation Network.

[24] Commission Recommendation (EU) 2019/243 of 6 February 2019 on a European Electronic Health Record exchange format

[25] IDC, The Digitization of the World: From Edge to Core, 2018

[26] NHS Digital is the statutory body in England with responsibility for national information and technology deployment in the health and care system

[27] Investment in software and databases as a % of non-residential Gross fixed capital formation (GFCF). GFCF is a measure of spending on fixed assets. Source: Calvino et al. (2018[26]),:”A taxonomy of digital intensive sectors

For more information, please contact:
Ray Pinto
Director for Digital Transformation
Vincenzo Renda
Senior Policy Manager for Digital Industrial Transformation
Thomas Hellebrand
Policy Officer, Digital Transformation
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