Creating a data resource: what will it take to build a medical information commons?

Ethical and social reflections on the proposed European Health Data Space

The COVID-19 pandemic demonstrated the benefits of international data sharing. Data sharing enabled the health care policy makers to make decisions based on real-time data, it enabled the tracking of the virus, and importantly it enabled the development of vaccines that were crucial to mitigating the impact of the virus. This data sharing is not the norm as data sharing needs to navigate complex ethical and legal rules, and in particular, the fragmented application of the General Data Protection Regulation (GDPR). The introduction of the draft regulation for a European Health Data Space (EHDS) in May 2022 seeks to address some of these legal issues. If passed, it will create an obligation to share electronic health data for certain secondary purposes. While there is a clear need to address the legal complexities involved with data sharing, it is critical that any proposed reforms are in line with ethical principles and the expectations of the data subjects. In this paper we offer a critique of the EHDS and offer some recommendations for this evolving regulatory space.

Opportunities and challenges of 5G network technology toward precision medicine

Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.

What can data trusts for health research learn from participatory governance in biobanks?

New models of data governance for health data are a focus of growing interest in an era of challenge to the social licence. In this article, we reflect on what the data trust model, which is founded on principles of participatory governance, can learn from experiences of involving and engagement of members of the public and participants in the governance of large-scale biobanks. We distinguish between upstream and ongoing governance models, showing how they require careful design and operation if they are to deliver on aspirations for deliberation and participation. Drawing on this learning, we identify a set of considerations important to future design for data trusts as they seek to ensure just, proportionate and fair governance. These considerations relate to the timing of involvement of participants, patterns of inclusion and exclusion, and responsiveness to stakeholder involvement and engagement. We emphasise that the evolution of governance models for data should be matched by a commitment to evaluation.

Considerations for an integrated population health databank in Africa: lessons from global best practices

Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa.

Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing.

Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing.  We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access.

Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.

Towards a Responsible Transition to Learning Healthcare Systems in Precision Medicine: Ethical Points to Consider

Learning healthcare systems have recently emerged as a strategy to continuously use experiences and outcomes of clinical care for research purposes in precision medicine. Although it is known that learning healthcare transitions in general raise important ethical challenges, the ethical ramifications of such transitions in the specific context of precision medicine have not extensively been discussed. Here, we describe three levers that institutions can pull to advance learning healthcare systems in precision medicine: (1) changing testing of individual variability (such as genes); (2) changing prescription of treatments on the basis of (genomic) test results; and/or (3) changing the handling of data that link variability and treatment to clinical outcomes. Subsequently, we evaluate how patients can be affected if one of these levers are pulled: (1) patients are tested for different or more factors than before the transformation, (2) patients receive different treatments than before the transformation and/or (3) patients’ data obtained through clinical care are used, or used more extensively, for research purposes. Based on an analysis of the aforementioned mechanisms and how these potentially affect patients, we analyze why learning healthcare systems in precision medicine need a different ethical approach and discuss crucial points to consider regarding this approach.

Researcher Perspectives on Data Sharing in Deep Brain Stimulation

The expansion of research on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises important neuroethics and policy questions related to data sharing. However, there has been little empirical research on the perspectives of experts developing these technologies. We conducted semi-structured, open-ended interviews with aDBS researchers regarding their data sharing practices and their perspectives on ethical and policy issues related to sharing. Researchers expressed support for and a commitment to sharing, with most saying that they were either sharing their data or would share in the future and that doing so was important for advancing the field. However, those who are sharing reported a variety of sharing partners, suggesting heterogeneity in sharing practices and lack of the broad sharing that would reflect principles of open science. Researchers described several concerns and barriers related to sharing, including privacy and confidentiality, the usability of shared data by others, ownership and control of data (including potential commercialization), and limited resources for sharing. They also suggested potential solutions to these challenges, including additional safeguards to address privacy issues, standardization and transparency in analysis to address issues of data usability, professional norms and heightened cooperation to address issues of ownership and control, and streamlining of data transmission to address resource limitations. Researchers also offered a range of views on the sensitivity of neural activity data (NAD) and data related to mental health in the context of sharing. These findings are an important input to deliberations by researchers, policymakers, neuroethicists, and other stakeholders as they navigate ethics and policy questions related to aDBS research.

Dynamic Dossier in the Cloud: A Sociotechnical Architecture for a Real-Time and Metrics-Based Data Tracking System with Gene and Cell Therapies as a Case Study

Background

Data sharing among stakeholders in the development, access, and use of drug therapies is critical but the current system and process are inefficient.

Methods

We take a Systems Engineering approach with a realistic use case to propose a scalable design for multi-stakeholder data sharing.

Results

We make three major contributions to the drug development and healthcare communities: first, a methodology for developing a multi-stakeholder data sharing system, with its focus on high-level requirements that influence the design of the system architecture and technology choice; second, the development of a realistic use case for long-term patient and therapy data tracking and sharing in the use of potentially curative and durable gene and cell therapies. Further, a bridge for the ‘awareness gap’ was found between the payer (Payer is organization which takes care of financial and operational aspects (which include insurance plans, provider network) of providing health care to US citizens. Or refer to health care insurers.) and the regulator communities by illustrating the common data tracking needs, which highlights the need for coordinated data activities; and third, a proposed system architecture for scalable, multi-stakeholder data sharing. Next steps are briefly discussed.

Conclusion

We present a system design for multiple stakeholders such as the payer, the regulator, the developer (drug manufacturer), and the healthcare provider to share data for their decision-making. The stakeholder community would benefit from collaboratively moving the system development proposal forward for efficient and cost-effective data sharing.

Emerging health data platforms: From individual control to collective data governance

Health data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how “traditional” health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.

Essential requirements for establishing and operating data trusts: practical guidance co-developed by representatives from fifteen canadian organizations and initiatives

INTRODUCTION

Increasingly, the label "data trust" is being applied to repeatable mechanisms or approaches to sharing data in a timely, fair, safe, and equitable way. However, there is an absence of practical guidance regarding how to establish and operate a data trust.

AIM AND APPROACH

In December 2019, the Canadian Institute for Health Information and the Vector Institute for Artificial Intelligence convened a working meeting of 19 people representing 15 Canadian organizations/initiatives involved in data sharing, most of which focus on public sector health data. The objective was to identify essential requirements for the establishment and operation of data trusts in the Canadian context. Preliminary requirements were discussed during the meeting and then refined as authors contributed to this manuscript.

RESULTS

Twelve minimum specification requirements ("min specs") for data trusts were identified. The foundational min spec is that data trusts must meet all legal requirements, including legal authority to collect, hold or share data. In addition, there was agreement that data trusts must have (i) an accountable governing body to ensure that the data trust achieves its stated purpose and is transparent, (ii) comprehensive data management including clear processes and qualified individuals responsible for the collection, storage, access, disclosure and use of data, (iii) training and accountability requirements for all data users and (iv) ongoing public and stakeholder engagement.

CONCLUSIONS

Practical guidance for the establishment and operation of data trusts was articulated in the form of 12 min specs requirements. The 12 min specs are a starting point. Future work to refine and strengthen them with members of the public, companies, and additional research data stakeholders from within and outside of Canada, is recommended.

Where Does Open Science Lead Us During a Pandemic? A Public Good Argument to Prioritize Rights in the Open Commons

During the 2020 COVID-19 pandemic, open science has become central to experimental, public health, and clinical responses across the globe. Open science (OS) is described as an open commons, in which a right to science renders all possible scientific data for everyone to access and use. In this common space, capitalist platforms now provide many essential services and are taking the lead in public health activities. These neoliberal businesses, however, have a problematic role in the capture of public goods. This paper argues that the open commons is a community of rights, consisting of people and institutions whose interests mutually support the public good. If OS is a cornerstone of public health, then reaffirming the public good is its overriding purpose, and unethical platforms ought to be excluded from the commons and its benefits.

Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework

In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their 2013 article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to "sell" clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed. Rather than debate whether patients or provider organizations "own" the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.

User-focused data sharing agreements: a foundation for the genomic future

Data sharing agreements that clearly describe what individuals are agreeing to and what responsibilities data stewards will undertake are crucial for the establishment, maintenance, and flourishing of genomic datasets. To optimize genomic data resources, researchers, care professionals, and informaticians must regard system design, user objectives, and environmental considerations through users' eyes, identifying fundamental values on which to build and potential barriers to success that must be avoided. Design of agreements that promote desired data sharing and protect valuable data resources as necessary begins with a review of user interests and concerns. Nontraditional approaches for informed consent (eg, abbreviated informed consent, electronic informed consent, and dynamic consent) can facilitate achievement of data donors' privacy-related goals while making data available to researchers. Transparency in individual-researcher interactions, recognition and accommodation of cultural differences, and identification of shared needs and goals create a foundation for data sharing agreements that work over short and long terms.

Responsible data sharing in international health research: a systematic review of principles and norms

BACKGROUND

Large-scale linkage of international clinical datasets could lead to unique insights into disease aetiology and facilitate treatment evaluation and drug development. Hereto, multi-stakeholder consortia are currently designing several disease-specific translational research platforms to enable international health data sharing. Despite the recent adoption of the EU General Data Protection Regulation (GDPR), the procedures for how to govern responsible data sharing in such projects are not at all spelled out yet. In search of a first, basic outline of an ethical governance framework, we set out to explore relevant ethical principles and norms.

METHODS

We performed a systematic review of literature and ethical guidelines for principles and norms pertaining to data sharing for international health research.

RESULTS

We observed an abundance of principles and norms with considerable convergence at the aggregate level of four overarching themes: societal benefits and value; distribution of risks, benefits and burdens; respect for individuals and groups; and public trust and engagement. However, at the level of principles and norms we identified substantial variation in the phrasing and level of detail, the number and content of norms considered necessary to protect a principle, and the contextual approaches in which principles and norms are used.

CONCLUSIONS

While providing some helpful leads for further work on a coherent governance framework for data sharing, the current collection of principles and norms prompts important questions about how to streamline terminology regarding de-identification and how to harmonise the identified principles and norms into a coherent governance framework that promotes data sharing while securing public trust.

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data

Data commons collate data with cloud computing infrastructure and commonly used software services, tools, and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical data. Over the past few years, data commons have been used to analyze, harmonize, and share large-scale genomics datasets. Data ecosystems can be built by interoperating multiple data commons. It can be quite labor intensive to curate, import, and analyze the data in a data commons. Data lakes provide an alternative to data commons and simply provide access to data, with the data curation and analysis deferred until later and delegated to those that access the data. We review software platforms for managing, analyzing, and sharing genomic data, with an emphasis on data commons, but also cover data ecosystems and data lakes.

Medical Information Commons to Support Learning Healthcare Systems: Examples From Canada

We explore how principles predicting the success of a medical information commons (MIC) advantaged or disadvantaged three MIC initiatives in three Canadian provinces. Our MIC case examples demonstrate that practices and policies to promote access to and use of health information can help improve individual healthcare and inform a learning health system. MICs were constrained by heterogenous health information protection laws across jurisdictions and risk-averse institutional cultures. A networked approach to MICs would unlock even more potential for national and international data collaborations to improve health and healthcare.

Hopeful and Concerned: Public Input on Building a Trustworthy Medical Information Commons

A medical information commons (MIC) is a networked data environment utilized for research and clinical applications. At three deliberations across the U.S., we engaged 75 adults in two-day facilitated discussions on the ethical and social issues inherent to sharing data with an MIC. Deliberants made recommendations regarding opt-in consent, transparent data policies, public representation on MIC governing boards, and strict data security and privacy protection. Community engagement is critical to earning the public's trust.

What is a Medical Information Commons?

A 2011 National Academies of Sciences report called for an "Information Commons" and a "Knowledge Network" to revolutionize biomedical research and clinical care. We interviewed 41 expert stakeholders to examine governance, access, data collection, and privacy in the context of a medical information commons. Stakeholders' attitudes about MICs align with the NAS vision of an Information Commons; however, differences of opinion regarding clinical use and access warrant further research to explore policy and technological solutions.

Importance of Participant-Centricity and Trust for a Sustainable Medical Information Commons

Drawing on a landscape analysis of existing data-sharing initiatives, in-depth interviews with expert stakeholders, and public deliberations with community advisory panels across the U.S., we describe features of the evolving medical information commons (MIC). We identify participant-centricity and trustworthiness as the most important features of an MIC and discuss the implications for those seeking to create a sustainable, useful, and widely available collection of linked resources for research and other purposes.

Who Owns the Data in a Medical Information Commons?

In this paper, we explore the perspectives of expert stakeholders about who owns data in a medical information commons (MIC) and what rights and interests ought to be recognized when developing a governance structure for an MIC. We then examine the legitimacy of these claims based on legal and ethical analysis and explore an alternative framework for thinking about participants' rights and interests in an MIC.

Characterizing the Biomedical Data-Sharing Landscape

Advances in technologies and biomedical informatics have expanded capacity to generate and share biomedical data. With a lens on genomic data, we present a typology characterizing the data-sharing landscape in biomedical research to advance understanding of the key stakeholders and existing data-sharing practices. The typology highlights the diversity of data-sharing efforts and facilitators and reveals how novel data-sharing efforts are challenging existing norms regarding the role of individuals whom the data describe.

The Role of Participants in a Medical Information Commons

Meaningful participant engagement has been identified as a key contributor to the success of efforts to share data via a "Medical Information Commons" (MIC). We present findings from expert stakeholder interviews aimed at understanding barriers to engagement and the appropriate role of MIC participants. Although most interviewees supported engagement, they distinguished between individual versus collective forms. They also noted challenges including representation and perceived inefficiency, prompting reflection on political aspects of engagement and efficiency concerns.

Genomic Data-Sharing Practices

Making data broadly accessible is essential to creating a medical information commons (MIC). Transparency about data-sharing practices can cultivate trust among prospective and existing MIC participants. We present an analysis of 34 initiatives sharing DNA-derived data based on public information. We describe data-sharing practices captured, including practices related to consent, privacy and security, data access, oversight, and participant engagement. Our results reveal that data-sharing initiatives have some distance to go in achieving transparency.

Beyond the Helix: Ethical, Legal, and Social Implications in Genomics

OBJECTIVE

To review ethical, legal, and social implications of genomics, a ground-breaking science that when applied improves cancer care outcomes.

DATA SOURCES

PubMed, Cumulative Index to Nursing and Allied Health (CINAHL), Cochrane Library, consensus statements, and professional guidelines.

CONCLUSION

Ethical, legal, and social domains of genomics are not fully delineated. Areas needing further discussion and policies include return of findings, informed consent, electronic health records, and data resources and sharing.

IMPLICATIONS FOR NURSING PRACTICE

All nurses need a basic understanding of the ethical, legal, and social implications of genomics.

Genetic Test Availability And Spending: Where Are We Now? Where Are We Going?

Genetic testing and spending on that testing have grown rapidly since the mapping of the human genome in 2003. However, it is not widely known how many tests there are, how they are used, and how they are paid for. Little evidence from large data sets about their use has emerged. We shed light on the issue of genetic testing by providing an overview of the testing landscape. We examined test availability and spending for the full spectrum of genetic tests, using unique data sources on test availability and commercial payer spending for privately insured populations, focusing particularly on tests measuring multiple genes in the period 2014-17. We found that there were approximately 75,000 genetic tests on the market, with about ten new tests entering the market daily. Prenatal tests accounted for the highest percentage of spending on genetic tests, and spending on hereditary cancer tests accounted for the second-highest. Our results provide insights for those interested in assessing genetic testing markets, test usage, and health policy implications, including current debates over the most appropriate regulatory and payer coverage mechanisms.

THE GENETIC INFORMATION NONDISCRIMINATION ACT AT AGE 10: GINA'S CONTROVERSIAL ASSERTION THAT DATA TRANSPARENCY PROTECTS PRIVACY AND CIVIL RIGHTS

The genomic testing industry is an edifice built on data transparency: transparent and often unconsented sharing of our genetic information with researchers to fuel scientific discovery, transparent sharing of our test results to help regulators infer whether the tests are safe and effective, and transparent sharing of our health information to help treat other patients on the premise that we gain reciprocity of advantage when each person's health care is informed by the best available data about all of us. Transparency undeniably confers many social benefits but creates risks to the civil rights of the people whose genetic information is shared. Touted as a major civil rights law at the time of its passage, the Genetic Information Nondiscrimination Act of 2008 (GINA) has endured ten years of criticism that its protections are ineffectual, insufficient, or even unethical and overtly unsafe for the people it aims to protect. At the center of this controversy are provisions of GINA that expand people's access to genetic information that others store about them-a heavily contested assertion that data transparency implies sharing data not just with third parties, but with the people whose data are being shared. This Article traces the decades-long roots of this assertion and explores pathways to resolve the controversy that engulfs it. It is important to resolve this controversy. As GINA enters its second decade, genomics is finally starting to gain sufficient predictive power to support discriminatory and other nefarious uses that GINA was designed to prevent. We are entering a positive feedback loop in which the genomic research that exposes us to risk of unwanted data disclosures simultaneously fuels discoveries that make such disclosures potentially more damaging.

Through Patients' Eyes: Regulation, Technology, Privacy, and the Future

Privacy is commonly regarded as a regulatory requirement achieved via technical and organizational management practices. Those working in the field of informatics often play a role in privacy preservation as a result of their expertise in information technology, workflow analysis, implementation science, or related skills. Viewing privacy from the perspective of patients whose protected health information is at risk broadens the considerations to include the perceived duality of privacy; the existence of privacy within a context unique to each patient; the competing needs inherent within privacy management; the need for particular consideration when data are shared; and the need for patients to control health information in a global setting. With precision medicine, artificial intelligence, and other treatment innovations on the horizon, health care professionals need to think more broadly about how to preserve privacy in a health care environment driven by data sharing. Patient-reported privacy preferences, privacy portability, and greater transparency around privacy-preserving functionalities are potential strategies for ensuring that privacy regulations are met and privacy is preserved.

The Genomic Commons

Over its 30 or so years of existence, the genomic commons-the worldwide collection of publicly accessible repositories of human and nonhuman genomic data-has enjoyed remarkable, perhaps unprecedented, success. Thanks to the rapid public data release policies initiated by the Human Genome Project, free access to a vast array of scientific data is now the norm, not only in genomics, but in scientific disciplines of all descriptions. And far from being a monolithic creation of bureaucratic fiat, the genomic commons is an exemplar of polycentric, multistakeholder governance. But like all dynamic and rapidly evolving systems, the genomic commons is not without its challenges. Issues involving scientific priority, intellectual property, individual privacy, and informed consent, in an environment of data sets of exponentially expanding size and complexity, must be addressed in the near term. In this review, we describe the characteristics and unique history of the genomic commons, then address some of the trends, challenges, and opportunities that we envision for this valuable public resource in the years to come.

HIPAA's Individual Right of Access to Genomic Data: Reconciling Safety and Civil Rights

In 2014, the United States granted individuals a right of access to their own laboratory test results, including genomic data. Many observers feel that this right is in tension with regulatory and bioethical standards designed to protect the safety of people who undergo genomic testing. This commentary attributes this tension to growing pains within an expanding federal regulatory program for genetic and genomic testing. The Genetic Information Nondiscrimination Act of 2008 expanded the regulatory agenda to encompass civil rights and consumer safety. The individual access right, as it applies to genomic data, is best understood as a civil-rights regulation. Competing regulatory objectives-safety and civil rights-were not successfully integrated during the initial rollout of genomic civil-rights regulations after 2008. Federal law clarifies how to prioritize safety and civil rights when the two come into conflict, although with careful policy design, the two need not collide. This commentary opens a dialog about possible solutions to advance safety and civil rights together.