Who Owns the Data in a Medical Information Commons?

Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth

Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.

Health Care AI and Patient Privacy-Dinerstein v Google

This Viewpoint summarizes a recent lawsuit alleging that a hospital violated patients’ privacy by sharing electronic health record (EHR) data with Google for development of medical artificial intelligence (AI) and discusses how the federal court’s decision in the case provides key insights for hospitals planning to share EHR data with for-profit companies developing medical AI.

What is meant by 'integrated personalized diabetes management': A view into the future and what success should look like

Integrated personalized diabetes management (IPDM) has emerged as a promising approach to improving outcomes in patients with diabetes mellitus (DM). This care approach emphasizes the integration and coordination of different providers, including physicians, nurses, dietitians, social workers and pharmacists. The goal of IPDM is to provide patients with personalized care that is tailored to their needs. This review addresses the concept of integrated care and the use of technology (including data, software applications and artificial intelligence) as well as managerial, regulatory and financial aspects. The implementation and upscaling of digitally enabled IPDM are discussed, with elaboration of successful practices and related evidence. Finally, recommendations are made. It is concluded that the adoption of digitally enabled IPDM on a global level is inevitable, considering the challenges created by an increasing prevalence of patients with DM and the need for better outcomes and improvement of health system sustainability.

Starting the Conversation Around the Ethical Use of Artificial Intelligence in Applied Behavior Analysis

Artificial intelligence (AI) is increasingly a part of our everyday lives. Though much AI work in healthcare has been outside of applied behavior analysis (ABA), researchers within ABA have begun to demonstrate many different ways that AI might improve the delivery of ABA services. Though AI offers many exciting advances, absent from the behavior analytic literature thus far is conversation around ethical considerations when developing, building, and deploying AI technologies. Further, though AI is already in the process of coming to ABA, it is unknown the extent to which behavior analytic practitioners are familiar (and comfortable) with the use of AI in ABA. The purpose of this article is twofold. First, to describe how existing ethical publications (e.g., BACB Code of Ethics) do and do not speak to the unique ethical concerns with deploying AI in everyday, ABA service delivery settings. Second, to raise questions for consideration that might inform future ethical guidelines when developing and using AI in ABA service delivery. In total, we hope this article sparks proactive dialog around the ethical use of AI in ABA before the field is required to have a reactionary conversation.

The wisdom of claiming ownership of human genomic data: A cautionary tale for research institutions

This article considers the practical question of how research institutions should best structure their legal relationship with the human genomic data that they generate. The analysis, based on South African law, is framed by the legal position that although a research institution that generates human genomic data is not automatically the owner thereof, it is well positioned to claim ownership of newly generated data instances. Given that the research institution exerts effort to generate the data, it can be argued that it has a moral right to claim ownership of such data. Combined with the fact that it has an interest in having comprehensive rights in such data, it appears that the prudent policy for research institutions is to claim ownership of the human genomic data instances that they generate. This policy is tested against two opposing policy positions. The first opposing policy position is that research participants should own the data that relate to them. However, in light of data protection legislation that already provides extensive protections to research participants, bestowing data ownership on research participants would offer little benefit to such individuals, while leading to significant practical problems for research institutions. The second opposing policy position is that the concept of ownership should be abandoned in favour of data custodianship. This opposing position is problematic, as avoiding reference to ownership is a denial of legal reality and hence not a useful policy. Also, avoiding reference to ownership will leave research institutions with limited legal remedies in the event of appropriation of data by third parties. Accordingly, it is concluded that the wisest policy for research institutions is indeed to explicitly claim ownership of the human genomic data instances that they generate.

Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits

In early 2023, the National Institutes of Health (NIH) implemented its Data Management and Sharing (DMS) Policy, requiring researchers to share scientific data produced with NIH funding. The policy's objective is to amplify the benefits of public investment in research by promoting the dissemination and reusability of primary data. Given this backdrop, identifying a robust methodology to assess the impact of data sharing across diverse research domains is essential. In this review, we adopted two methodological paradigms, the bottom-up and top-down strategies, and employed content analysis to pinpoint established methodologies and innovative practices within this intricate field. Although numerous author-level metrics are available to gauge the impact of data sharing, their application is still limited. Non-traditional metrics, encompassing economic (e.g., cost savings) and intangible benefits, presently appear to hold more potential for evaluating the impact of primary data sharing. Finally, we address the primary obstacles encountered by open data policies and introduce an innovative "Shared model for shared data" framework to bolster data sharing practices and refine evaluation metrics.

Cloud-based biomedical data storage and analysis for genomic research: Landscape analysis of data governance in emerging NIH-supported platforms

The storage, sharing, and analysis of genomic data poses technical and logistical challenges that have precipitated the development of cloud-based computing platforms designed to facilitate collaboration and maximize the scientific utility of data. To understand cloud platforms' policies and procedures and the implications for different stakeholder groups, in summer 2021, we reviewed publicly available documents (N = 94) sourced from platform websites, scientific literature, and lay media for five NIH-funded cloud platforms (the All of Us Research Hub, NHGRI AnVIL, NHLBI BioData Catalyst, NCI Genomic Data Commons, and the Kids First Data Resource Center) and a pre-existing data sharing mechanism, dbGaP. Platform policies were compared across seven categories of data governance: data submission, data ingestion, user authentication and authorization, data security, data access, auditing, and sanctions. Our analysis finds similarities across the platforms, including reliance on a formal data ingestion process, multiple tiers of data access with varying user authentication and/or authorization requirements, platform and user data security measures, and auditing for inappropriate data use. Platforms differ in how data tiers are organized, as well as the specifics of user authentication and authorization across access tiers. Our analysis maps elements of data governance across emerging NIH-funded cloud platforms and as such provides a key resource for stakeholders seeking to understand and utilize data access and analysis options across platforms and to surface aspects of governance that may require harmonization to achieve the desired interoperability.

Is there a civic duty to support medical AI development by sharing electronic health records?

Medical artificial intelligence (AI) is considered to be one of the most important assets for the future of innovative individual and public health care. To develop innovative medical AI, it is necessary to repurpose data that are primarily generated in and for the health care context. Usually, health data can only be put to a secondary use if data subjects provide their informed consent (IC). This regulation, however, is believed to slow down or even prevent vital medical research, including AI development. For this reason, a number of scholars advocate a moral civic duty to share electronic health records (EHRs) that overrides IC requirements in certain contexts. In the medical AI context, the common arguments for such a duty have not been subjected to a comprehensive challenge. This article sheds light on the correlation between two normative discourses concerning informed consent for secondary health record use and the development and use of medical AI. There are three main arguments in favour of a civic duty to support certain developments in medical AI by sharing EHRs: the 'rule to rescue argument', the 'low risks, high benefits argument', and the 'property rights argument'. This article critiques all three arguments because they either derive a civic duty from premises that do not apply to the medical AI context, or they rely on inappropriate analogies, or they ignore significant risks entailed by the EHR sharing process and the use of medical AI. Given this result, the article proposes an alternative civic responsibility approach that can attribute different responsibilities to different social groups and individuals and that can contextualise those responsibilities for the purpose of medical AI development.

"Idealists and capitalists": ownership attitudes and preferences in genomic citizen science

The perspectives of genomic citizen scientists on ownership of research outputs are not well understood, yet they are useful for identifying alignment of participant expectations and project practices and can help guide efforts to develop innovative tools and strategies for managing ownership claims. Here, we report findings from 52 interviews conducted in 2018 and 2019 to understand genomic citizen science stakeholders' conceptualizations of, experiences with, and preferences for ownership of research outputs. Interviewees identified four approaches for recognizing genomic citizen scientists' ownership and related credit interests in research outputs: shared governance via commons models; fractional ownership of benefits; full and creative attribution; and offensive and defensive patenting. Interviewees also agreed that the model selected by any project should at least maximize access to research outputs and, as appropriate and to the extent possible, broadly distribute rights of control and entitlements to research benefits.

'It’s not something you can take in your hands'. Swiss experts’ perspectives on health data ownership: an interview-based study

Objectives

The evolution of healthcare and biomedical research into data-rich fields has raised several questions concerning data ownership. In this paper, we aimed to analyse the perspectives of Swiss experts on the topic of health data ownership and control.

Design

In our qualitative study, we selected participants through purposive and snowball sampling. Interviews were recorded, transcribed verbatim and then analysed thematically.

Setting

Semi-structured interviews were conducted in person, via phone or online.

Participants

We interviewed 48 experts (researchers, policy makers and other stakeholders) of the Swiss health-data framework.

Results

We identified different themes linked to data ownership. These include: (1) the data owner: data-subjects versus data-processors; (2) uncertainty about data ownership; (3) labour as a justification for data ownership and (4) the market value of data. Our results suggest that experts from Switzerland are still divided about who should be the data owner and also about what ownership would exactly mean. There is ambivalence between the willingness to acknowledge patients as the data owners and the fact that the effort made by data-processors (eg, researchers) to collect and manage the data entitles them to assert ownership claims towards the data themselves. Altogether, a tendency to speak about data in market terms also emerged.

Conclusions

The development of a satisfactory account of data ownership as a concept to organise the relationship between data-subjects, data-processors and data themselves is an important endeavour for Switzerland and other countries who are developing data governance in the healthcare and research domains. Setting clearer rules on who owns data and on what ownership exactly entails would be important. If this proves unfeasible, the idea that health data cannot truly belong to anyone could be promoted. However, this will not be easy, as data are seen as an asset to control and profit from.

Fourth Amendment Protections of Health Information After Carpenter v. United States: The Devil's In The Database

Every day, companies collect health information from customers and analyze it for commercial purposes. This poses a significant threat to privacy, particularly as the Fourth Amendment protection of this deeply personal information is limited. Generally, law enforcement officers do not need probable cause and a warrant to access these private health information databases; only a subpoena is required, and sometimes nothing at all. The Fourth Amendment protections for health information may, however, have changed after the Supreme Court's 2018 decision in Carpenter v. United States, which held that the Fourth Amendment protects people from warrantless searches of historical cell-site location information possessed by their cell phone providers. The Court explained that, because of the nature of historical cell-site location information, individuals retain a reasonable expectation of privacy despite the information being in the possession of a third party. In reaching its holding, the Supreme Court considered the type of data, the uniqueness of cell-site location information, the impact of technological advancement on privacy, the voluntariness of the disclosure, and the property rights associated with the records. Many of these factors could support heightened Fourth Amendment protection for health information. This Article argues that Carpenter v. United States provides additional protections for future searches of health information in private databases.

A FAUSTIAN BARGAIN THAT UNDERMINES RESEARCH PARTICIPANTS' PRIVACY RIGHTS AND RETURN OF RESULTS

A 2018 committee report published by the highly respected National Academies of Science, Engineering, and Medicine (the Report) recommends stripping research participants of crucial data privacy rights and discarding decades of carefully deliberated consensus guidelines for the ethical return of results and data from research. This Article traces these disturbing recommendations to three root causes: (1) a statement of task that blocked careful and impartial analysis of a disputed legal matter central to the Report; (2) a piecemeal legal analysis that omitted relevant strands of law; and (3) the inappropriate conflation of two distinct concepts-the return of individual research results (the stated subject of the Report) and privacy-enabling individual access rights, which have a nearly fifty-year legal history long predating the modern debate about return of results. The Report's recommendations would erect new barriers to the return of results and, simultaneously, dial back a core data privacy right that Americans-including many research participants-currently enjoy. We urge extreme caution in implementing this Report's flawed recommendations. Congress has elevated the right to see one's personal information to the status of a civil right in many different data environments. Diminishing individual access in the research context erodes its status as a right more broadly.

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.