Will the European Health Data Space change data sharing rules?

Conceptualizing fairness in the secondary use of health data for research: A scoping review

With the introduction of the European Health Data Space (EHDS), the secondary use of health data for research purposes is attracting more attention. Secondary health data processing promises to address novel research questions, inform the design of future research and improve healthcare delivery generally. To comply with the existing data protection regulations, the secondary data use must be fair, among other things. However, there is no clear understanding of what fairness means in the context of secondary use of health data for scientific research purposes. In response, we conducted a scoping review of argument-based literature to explore how fairness in the secondary use of health data has been conceptualized. A total of 35 publications were included in the final synthesis after abstract and full-text screening. Using an inductive approach and a thematic analysis, our review has revealed that balancing individual and public interests, reducing power asymmetries, setting conditions for commercial involvement, and implementing benefit sharing are essential to guarantee fair secondary use research. The findings of this review can inform current and future research practices and policy development to adequately address concerns about fairness in the secondary use of health data.

Secondary Use of Health Data for Medical AI: A Cross-Regional Examination of Taiwan and the EU

This paper conducts a comparative analysis of data governance mechanisms concerning the secondary use of health data in Taiwan and the European Union (EU). Both regions have adopted distinctive approaches and regulations for utilizing health data beyond primary care, encompassing areas such as medical research and healthcare system enhancement. Through an examination of these models, this study seeks to elucidate the strategies, frameworks, and legal structures employed by Taiwan and the EU to strike a delicate balance between the imperative of data-driven healthcare innovation and the safeguarding of individual privacy rights. This paper examines and compares several key aspects of the secondary use of health data in Taiwan and the EU. These aspects include data governance frameworks, legal and regulatory frameworks, data access and sharing mechanisms, and privacy and security considerations. This comparative exploration offers invaluable insights into the evolving global landscape of health data governance. It provides a deeper understanding of the strategies implemented by these regions to harness the potential of health data while upholding the ethical and legal considerations surrounding its secondary use. The findings aim to inform best practices for responsible and effective health data utilization, particularly in the context of medical AI applications.

OHDSI-compliance: a set of document templates facilitating the implementation and operation of a software stack for real-world evidence generation

Introduction

The open-source software offered by the Observational Health Data Science and Informatics (OHDSI) collective, including the OMOP-CDM, serves as a major backbone for many real-world evidence networks and distributed health data analytics platforms. While container technology has significantly simplified deployments from a technical perspective, regulatory compliance can remain a major hurdle for the setup and operation of such platforms. In this paper, we present OHDSI-Compliance, a comprehensive set of document templates designed to streamline the data protection and information security-related documentation and coordination efforts required to establish OHDSI installations.

Methods

To decide on a set of relevant document templates, we first analyzed the legal requirements and associated guidelines with a focus on the General Data Protection Regulation (GDPR). Moreover, we analyzed the software architecture of a typical OHDSI stack and related its components to the different general types of concepts and documentation identified. Then, we created those documents for a prototypical OHDSI installation, based on the so-called Broadsea package, following relevant guidelines from Germany. Finally, we generalized the documents by introducing placeholders and options at places where individual institution-specific content will be needed.

Results

We present four documents: (1) a record of processing activities, (2) an information security concept, (3) an authorization concept, as well as (4) an operational concept covering the technical details of maintaining the stack. The documents are publicly available under a permissive license.

Discussion

To the best of our knowledge, there are no other publicly available sets of documents designed to simplify the compliance process for OHDSI deployments. While our documents provide a comprehensive starting point, local specifics need to be added, and, due to the heterogeneity of legal requirements in different countries, further adoptions might be necessary.

UNCAN.eu: Toward a European Federated Cancer Research Data Hub

To enable a collective effort that generates a new level of UNderstanding CANcer (UNCAN.eu) [Cancer Discov (2022) 12 (11): OF1], the European Union supports the creation of a sustainable platform that connects cancer research across Member States. A workshop hosted in Heidelberg gathered European cancer experts to identify ongoing initiatives that may contribute to building this platform and discuss the governance and long-term evolution of a European Federated Cancer Data Hub.

Population Neuroscience: Strategies to Promote Data Sharing While Protecting Privacy

Population neuroscience aims to advance our understanding of how genetic and environmental factors influence brain development and brain health over the life span, by integrating genomics, epidemiology, and neuroscience at population scale. This big data approach depends on data sharing strategies at both the micro- and macro-level, as well as attention to effective data management and protection of participant privacy. At the micro-level, researchers participate in international consortia that support collaboration, standards, and data sharing. They also seek to link together cohort studies, administrative health databases, and measures of the physical, built, and social environment in creative ways. Large-scale, longitudinal, and multi-modal cohorts are being designed to support explorations of genetic and environmental impacts on the brain. At a macro-level, funding agency policies now require data across health research domains to be managed according to the FAIR (findable, accessible, interoperable, and re-useable) Data principles and made available to the research community in a timely manner to support reproducibility and re-use. Data repositories provide technical infrastructure for storing, accessing, and increasingly also analyzing rich population-level data. Federated and cloud-based approaches are being leveraged to improve the security, remote accessibility, and performance of repositories. Finally, legal frameworks are being developed to facilitate secure health data access, integration, and analysis, providing new opportunities for the field.

A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability

Introduction

Europe works to improve cancer management through the use of artificialintelligence (AI), and there is a need to accelerate the development of AI applications for childhood cancer. However, the current strategies used for algorithm development in childhood cancer may have bias and limited generalizability. This study reviewed existing publications on AI tools for pediatric brain tumors, Europe's most common type of childhood solid tumor, to examine the data sources for developing AI tools.

Methods

We performed a bibliometric analysis of the publications on AI tools for pediatric brain tumors, and we examined the type of data used, data sources, and geographic location of cohorts to evaluate the generalizability of the algorithms.

Results

We screened 10503 publications, and we selected 45. A total of 34/45 publications developing AI tools focused on glial tumors, while 35/45 used MRI as a source of information to predict the classification and prognosis. The median number of patients for algorithm development was 89 for single-center studies and 120 for multicenter studies. A total of 17/45 publications used pediatric datasets from the UK.

Discussion

Since the development of AI tools for pediatric brain tumors is still in its infancy, there is a need to support data exchange and collaboration between centers to increase the number of patients used for algorithm training and improve their generalizability. To this end, there is a need for increased data exchange and collaboration between centers and to explore the applicability of decentralized privacy-preserving technologies consistent with the General Data Protection Regulation (GDPR). This is particularly important in light of using the European Health Data Space and international collaborations.

Depression screening tool accuracy individual participant data meta-analyses: data contribution was associated with multiple factors

OBJECTIVES

To examine the proportion of eligible primary studies that contributed data, study characteristics associated with data contribution, and reasons for noncontribution using diagnostic test accuracy Individual Participant Data Meta-Analysis (IPDMA) data sets from the DEPRESsion Screening Data project.

STUDY DESIGN AND SETTING

We reviewed data set contributions from four IPDMAs. A multivariable logistic regression model was fitted to evaluate study factors associated with data contribution.

RESULTS

Of 456 eligible studies from four included IPDMAs, 295 (65%) contributed data. More recent year of publication and higher journal impact factor were associated with greater odds of data contribution. Studies conducted in Europe (excluding the United Kingdom), Oceania, Canada, the Middle East, Africa, and Central or South America (reference = the United States), that have recruitment from inpatient care or nonmedical settings (reference = outpatient), that reported screening accuracy results, or that drew negative conclusions (reference = positive conclusions) were more likely to contribute data. Studies of the Geriatric Depression Scale (reference = the Patient Health Questionnaire) or lacking funding information were negatively associated with data contribution. Over 80% of noncontributions were due to authors being unreachable or data being unavailable.

CONCLUSION

The study identified factors associated with data contribution that may support future research to promote data contribution to IPDMAs.

The European health data space: Too big to succeed?

In May 2022, the European Commission issued the Proposal for a Regulation on the European Health Data Space (EHDS), with the aims of granting citizens increased access to and control of their (electronic) health data across the EU, and facilitating health data re-use for research, innovation, and policymaking. As the first in a series of European domain-specific "data spaces", the EHDS is a high-stakes development that will transform health data governance in the EU region. As an international consortium of experts from health policy, law, ethics and the social sciences, we are concerned that the EHDS Proposal will detract from, rather than lead to the achievement of, its stated aims. We are in no doubt on the benefits of using health data for secondary purposes, and we appreciate attempts to facilitate such uses across borders in a carefully curated manner. Based on the current draft Regulation, however, the EHDS risks undermining rather than enhancing patient control over data; hindering rather than facilitating the work of health professionals and researchers; and eroding rather than increasing the public value generated through health data sharing. Therefore, significant adjustments are needed if the EHDS is to realize its promised benefits. Besides analyzing the implications for key groups and European societies at large who will be affected by the implementation of the EHDS, this contribution advances targeted policy recommendations to address the identified shortcomings of the EHDS Proposal.

Strengthening Cybersecurity for Patient Data Protection in Europe

The health care sector experiences 76% of cybersecurity breaches due to basic web application attacks, miscellaneous errors, and system intrusions, resulting in compromised health data or disrupted health services. The European Commission proposed the European Health Data Space (EHDS) in 2022 to enhance care delivery and improve patients' lives by offering all European Union (EU) citizens control over their personal health data in a private and secure environment. The EU has taken an important step in homogenizing the health data environment of the European health ecosystem, although more attention needs to be paid to keeping the health data of EU citizens safe and secure within the EHDS. The pooling of health data across countries can have tremendous benefits, but it may also become a target for cybercriminals or state-sponsored hackers. State-of-the-art security measures are essential, and the current EHDS proposal lacks sufficient measures to warrant a cybersecure and resilient environment.

Development of an Evidence-Based Conceptual Model of the Health Care Sector Under Digital Transformation: Integrative Review

BACKGROUND

Digital transformation is currently one of the most influential developments. It is fundamentally changing consumers' expectations and behaviors, challenging traditional firms, and disrupting numerous markets. Recent discussions in the health care sector tend to assess the influence of technological implications but neglect other factors needed for a holistic view on the digital transformation. This calls for a reevaluation of the current state of digital transformation in health care. Consequently, there is a need for a holistic view on the complex interdependencies of digital transformation in the health care sector.

OBJECTIVE

This study aimed to examine the effects of digital transformation on the health care sector. This is accomplished by providing a conceptual model of the health care sector under digital transformation.

METHODS

First, the most essential stakeholders in the health care sector were identified by a scoping review and grounded theory approach. Second, the effects on these stakeholders were assessed. PubMed, Web of Science, and Dimensions were searched for relevant studies. On the basis of an integrative review and grounded theory methodology, the relevant academic literature was systematized and quantitatively and qualitatively analyzed to evaluate the impact on the value creation of, and the relationships among, the stakeholders. Third, the findings were synthesized into a conceptual model of the health care sector under digital transformation.

RESULTS

A total of 2505 records were identified from the database search; of these, 140 (5.59%) were included and analyzed. The results revealed that providers of medical treatments, patients, governing institutions, and payers are the most essential stakeholders in the health care sector. As for the individual stakeholders, patients are experiencing a technology-enabled growth of influence in the sector. Providers are becoming increasingly dependent on intermediaries for essential parts of the value creation and patient interaction. Payers are expected to try to increase their influence on intermediaries to exploit the enormous amounts of data while seeing their business models be challenged by emerging technologies. Governing institutions regulating the health care sector are increasingly facing challenges from new entrants in the sector. Intermediaries increasingly interconnect all these stakeholders, which in turn drives new ways of value creation. These collaborative efforts have led to the establishment of a virtually integrated health care ecosystem.

CONCLUSIONS

The conceptual model provides a novel and evidence-based perspective on the interrelations among actors in the health care sector, indicating that individual stakeholders need to recognize their role in the system. The model can be the basis of further evaluations of strategic actions of actors and their effects on other actors or the health care ecosystem itself.

Data sharing and data governance in sub-Saharan Africa: Perspectives from researchers and scientists engaged in data-intensive research

The data ecosystem is complex and involves multiple stakeholders. Researchers and scientists engaging in data-intensive research collect, analyse, store, manage and share large volumes of data. Consequently, capturing researchers' and scientists' views from multidisciplinary fields on data use, sharing and governance adds an important African perspective to emerging debates. We conducted a descriptive cross-sectional survey and received 160 responses from researchers and scientists representing 43 sub-Saharan African countries. Whilst most respondents were satisfied with institutional data storage processes, 40% indicated that their organisations or institutions did not have a formally established process for storing data beyond the life cycle of the project. Willingness to share data was generally high, but increased when data privacy was ensured. Robust governance frameworks increased the willingness to share, as did the regulation of access to data on shared platforms. Incentivising data sharing remains controversial. Respondents were satisfied with exchanging their data for co-authorship on publications (89.4%) and collaboration on projects (77.6%). However, respondents were split almost equally in terms of sharing their data for commercial gain. Regarding the process of managing data, 40.6% indicated that their organisations do not provide training on best practices for data management. This could be related to a lack of resources, chronic institutional under-investment, and suboptimal research training and mentorship in sub-Saharan Africa. The sustainability of data sharing may require ethical incentive structures to further encourage researchers and scientists. Tangible infrastructure to facilitate such sharing is a prerequisite. Capacity development in data governance for researchers and scientists is sorely needed.

Significance

Data sharing is necessary to advance science, yet there are many constraints. In this study, we explored factors that promote a willingness to share, as well as constraining factors. Seeking potential solutions to improve data sharing is a scientific and ethical imperative. The standardisation of basic data sharing and data transfer agreements, and the development of a Data Access Committee will strengthen data governance and facilitate responsible data sharing in sub-Saharan Africa. Funders, institutions, researchers and scientists ought to jointly contribute to fair and equitable data use and sharing during and beyond the life cycle of research projects.

The application of data altruism in clinical research through empirical and legal analysis lenses

Background

The legal framework for clinical research in the EU is complex and the lack of harmonization of the relevant legal and ethical rules remains one of the main challenges for stakeholders in the field. The recently adopted Data Governance Act (DGA) and the proposal for a European Health Data Space (EHDS) promise to solve the existing challenges with respect to access to and (re)use of personal data for research, but also risk to further complexify the field. The DGA introduced a novel mechanism - data altruism. Data altruism is understood as the voluntary sharing of personal and non-personal data, based on the consent of data subjects or the permission of natural and legal persons, without seeking a reward and for objectives of general interest. This study aimed to gain insights into the opinion of clinical research stakeholders on data altruism, and to critically discuss key issues pertaining to the application of data altruism from a legal point of view.

Methods

Semi-structured interviews with (1) data protection officers (DPOs) and legal experts working with commercial and academic sponsors of clinical trials, (2) investigators, and (3) members of research ethics committees. Data underwent framework analysis. The legal discussion was comprised of legal doctrinal research with focus on the DGA, EHDS proposal, and the interplay with the EU General Data Protection Regulation (GDPR).

Results

Fourteen experts took part in the interviews, more than half of which were DPOs/legal experts. Interviewees were based in seven EU Member states and the United Kingdom. The majority of participants were critical towards the data altruism mechanism and pointed out challenges and risks associated with its application.

Conclusion

Although data altruism holds the potential to facilitate data sharing, its application in clinical research at the moment is still riddled with uncertainties. The interplay of the DGA rules with the provisions of the GDPR and the EHDS proposal are insufficiently clear and further efforts from the legislator are required to build a working, patient-centered, and research fostering data altruism system.

Towards the European Health Data Space (EHDS) ecosystem: A survey research on future health data scenarios

OBJECTIVE

The European Health Data Space (EHDS) aims to provide better exchange and expand access to health data across Europe. In this way, the EHDS will support healthcare delivery (known as the "primary use of data") and facilitate access to health data for research and policy-making purposes (known as the "secondary use of data"). To achieve this goal, we need to build the required ecosystem of the EHDS with all healthcare stakeholders.

MATERIALS AND METHODS

We conducted a survey research study to explore the health informaticians' recommendations on future health data scenarios shaping the EHDS ecosystem. We created an anonymous- online questionnaire and disseminated it through wide international networks of health informaticians. In addition, we conducted a workshop during the Medical Informatics Europe Conference (MIE2022) and invited the attendees to complete the questionnaire during the workshop.

RESULTS

We received 43 responses to our questionnaire from 15 European Union (EU) countries and 7 non-EU countries. Most respondents described the current health data scenario in their countries as a traditional healthcare system with moderate growth (25.6 %, n = 11). The second selected scenario was the reinventing healthcare scenario in a data-driven one-world framework (23.3 %, n = 10).

DISCUSSION

The results of this work are matched with the findings of the recently published study on digital health implementation in the EU (conducted by the French government in April 2022). This also reflects the current ongoing efforts in the EU countries to deploy national infrastructure for health data management, exchange, and sharing.

CONCLUSIONS

Upon the respondents' recommendations, there is a strong need to support the health democratization scenarios in Europe, as the main driver for building the EHDS ecosystem.

Sensing the (digital) pulse. Future steps for improving the secondary use of data for research in Switzerland

Introduction

Ensuring that the health data infrastructure and governance permits an efficient secondary use of data for research is a policy priority for many countries. Switzerland is no exception and many initiatives have been launched to improve its health data landscape. The country now stands at an important crossroad, debating the right way forward. We aimed to explore which specific elements of data governance can facilitate - from ethico-legal and socio-cultural perspectives - the sharing and reuse of data for research purposes in Switzerland.

Methods

A modified Delphi methodology was used to collect and structure input from a panel of experts via successive rounds of mediated interaction on the topic of health data governance in Switzerland.

Results

First, we suggested techniques to facilitate data sharing practices, especially when data are shared between researchers or from healthcare institutions to researchers. Second, we identified ways to improve the interaction between data protection law and the reuse of data for research, and the ways of implementing informed consent in this context. Third, we put forth ideas on policy changes, such as the steps necessary to improve coordination between different actors of the data landscape and to win the defensive and risk-adverse attitudes widespread when it comes to health data.

Conclusions

After having engaged with these topics, we highlighted the importance of focusing on non-technical aspects to improve the data-readiness of a country (e.g., attitudes of stakeholders involved) and of having a pro-active debate between the different institutional actors, ethico-legal experts and society at large.

Empowering quality data - the gordian knot of bringing real innovation into healthcare system

OBJECTIVES

The introduction of Personalised Medicine (PM) into healthcare systems could benefit from a clearer understanding of the distinct national and regional frameworks around the world. Recent engagement by international regulators on maximising the use of real-world evidence (RWE) has highlighted the scope for improving the exploitation of the treasure-trove of health data that is currently largely neglected in many countries. The European Alliance for Personalised Medicine (EAPM) led an international study aimed at identifying the current status of conditions.

METHODS

A literature review examined how far such frameworks exist, with a view to identifying conducive factors - and crucial gaps. This extensive review of key factors across 22 countries and 5 regions revealed a wide variety of attitudes, approaches, provisions and conditions, and permitted the construction of a comprehensive overview of the current status of PM. Based on seven key pillars identified from the literature review and expert panels, the data was quantified, and on the basis of further analysis, an index was developed to allow comparison country by country and region by region.

RESULTS

The results show that United States of America is leading according to overall outcome whereas Kenya scored the least in the overall outcome.

CONCLUSIONS

Still, common approaches exist that could help accelerate take-up of opportunities even in the less prosperous parts of the world.

You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used “consent or anonymize approach” undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.