This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, similar to the way in which they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute at the University of Oxford. Ten arguments are provided to support the need to (...) foster posthumous medical data donation. Two major risks are also identified—harm to others, and lack of control over the use of data—which could follow from unregulated donation of medical data. The argument that record-based medical research should proceed without the need to secure informed consent is rejected, and instead a voluntary and participatory approach to using personal medical data should be followed. The analysis concludes by stressing the need to develop an ethical code for data donation to minimise the risks, and offers five foundational principles for ethical medical data donation suggested as a draft code. (shrink)

This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, in a way similar to how they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute. Ten arguments are provided to support the need to foster posthumous medical (...) class='Hi'>data donation. Two major risks are also identified—harm to others, and lack of control over the use of data—which could follow from unregulated donation of medical data. The argument that record-based medical research should proceed without the need to ask for informed consent is rejected, and it instead a voluntary and participatory approach to using personal medical data should be followed. The analysis concludes by stressing the need to develop an ethical code for data donation to minimise the risks providing five foundational principles for ethical medical data donation; and suggesting a draft for such a code. (shrink)

This open access book presents an ethical approach to utilizing personal medical data. It features essays that combine academic argument with practical application of ethical principles. The contributors are experts in ethics and law. They address the challenges in the re-use of medical data of the deceased on a voluntary basis. This pioneering study looks at the many factors involved when individuals and organizations wish to share information for research, policy-making, and humanitarian purposes. -/- Today, it (...) is easy to donate blood or even organs, but it is virtually impossible to donate one’s own medical data. This is seen as ethically unacceptable. Yet, data donation can greatly benefit the welfare of our societies. This collection provides timely interdisciplinary research on biomedical big data. Topics include the ethics of data donation, the legal and regulatory challenges, and the current and future collaborations. -/- Readers will learn about the ethical and regulatory challenges associated with medical data donations. They will also better understand the special nature of using deceased data for research purposes with regard to ethical principles of autonomy, beneficence, and justice. In addition, the contributors identify the key governance issues of such a scheme. The essays also look at what we can learn in terms of best practice from existing medical data schemes. (shrink)

This open access book presents an ethical approach to utilizing personal medical data. It features essays that combine academic argument with practical application of ethical principles. The contributors are experts in ethics and law. They address the challenges in the re-use of medical data of the deceased on a voluntary basis. This pioneering study looks at the many factors involved when individuals and organizations wish to share information for research, policy-making, and humanitarian purposes. Today, it is (...) easy to donate blood or even organs, but it is virtually impossible to donate one’s own medical data. This is seen as ethically unacceptable. Yet, data donation can greatly benefit the welfare of our societies. This collection provides timely interdisciplinary research on biomedical big data. Topics include the ethics of data donation, the legal and regulatory challenges, and the current and future collaborations. Readers will learn about the ethical and regulatory challenges associated with medical data donations. They will also better understand the special nature of using deceased data for research purposes with regard to ethical principles of autonomy, beneficence, and justice. In addition, the contributors identify the key governance issues of such a scheme. The essays also look at what we can learn in terms of best practice from existing medical data schemes. (shrink)

Data protection law has a linear logic, in that it purports to trace the lifecycle of personal data from creation to collection, processing, transfer, and ultimately its demise, and to regulate each step so as to promote the data subject’s control thereof. Big data defies this linear logic, in that it decontextualizes data from its original environment and conducts an algorithmic nonlinear mix, match, and mine analysis. Applying data protection law to the processing of (...) big data does not work well, to say the least. This Article examines the case of big medical data. A survey of emerging research practices indicates that studies either ignore data protection law altogether or assume an ex post position, namely that because they are conducted after the data has already been created in the course of providing medical care, and they use de-identified data, they go under the radar of data protection law. These studies focus on the end-point of the lifecycle of big data: if sufficiently anonymous at publication, the previous steps are overlooked, on the claim that they enjoy immunity. I argue that this answer is too crude. To portray data protection law in its best light, we should view it as a process-based attempt to equip data subjects with some power to control personal data about them, in all phases of data processing. Such control reflects the underlying justification of data protection law as an implementation of human dignity. The process-based approach fits current legal practices and is justified by reflecting dignitarian conceptions of informational privacy. (shrink)

Neurodevices that collect neural (or brain activity) data have been characterised as having the ability to register the inner workings of human mentality. There are concerns that the proliferation of such devices in the consumer-directed realm may result in the mass processing and commercialisation of neural data (as has been the case with social media data) and even threaten the mental privacy of individuals. To prevent this, some argue that all raw neural data should be conceptualised (...) and regulated as “medical data” even if it is collected from consumer-directed devices in obviously non-clinical settings. In this paper, we argue that without a clearer formulation of what does and does not count as medical data, this approach might also uncritically enlarge the scope for medical influence and an unwarranted medicalisation of everyday aspects of mental life. Indeed, if we were to accept the position that all neural data is medical data because it offers insights into personally sensitive information (such a person’s thoughts, emotions, or intentions) then this could even unnecessarily expand the boundaries of medical data to other forms of data that otherwise seem to be non-clinical. If all brain data (neural activity data) is considered to be medical data even when collected from consumer-directed devices then we might unintentionally reduce meaningful distinctions between what is and what is not rightfully in the purview of medicine. (shrink)

Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare (...) sector, this article presents a novel metaheuristic rough set-based feature selection with rule-based medical data classification technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%. (shrink)

Purpose The purpose of this paper is to review current practice in sharing and mining medical data revealing benefits, costs and ethical issues. Based on stakeholder perspectives and values, the authors create an ethical code to regulate the sharing and mining of medical information. Design/methodology/approach The framework is based on a review of academic, practitioner and legal research. Findings Owing to the inability of current safeguards to protect consumers from risks related to the disclosure of medical (...) information, the authors develop a framework for ethical sharing and mining of medical data, security, transparency, respect, accountability, community and quality, which espouses security, transparency, respect, accountability, community and quality as the basic tenets of ethical data sharing and mining practice. Research limitations/implications The STRACQ framework is an original, previously unpublished contribution that will require modification over time based on discussion and debate within and among the academy, medical community and public policymakers. Social implications The framework for sharing borrows from the Fair Credit Reporting Act, allowing the collection and dissemination of identified medical data but placing strict limitations on use. Following this framework, benefits of shared and mined medical data are freely available with appropriate safeguards for consumer privacy. Originality/value Mandates for adoption of electronic health-care records require an understanding of medical data mining. This paper presents a review of data mining techniques and reasons for engaging in the practice of identifying benefits, costs and ethical issues. The authors create an original framework, STRACQ, for ethical sharing and mining of medical information, allowing knowledge exploration while protecting consumer privacy. (shrink)

In 1947, the Cornell psychiatrist Keeve Brodman and a handful of colleagues began developing what would become one of the most widely used health questionnaires of its time—the Cornell Medical Index (CMI). A rigidly standardized form, the CMI presented 195 yes-no questions designed to capture the health status of “the total patient.” Over the following decades, Brodman’s project of standardizing medical history taking gradually evolved into a project of mathematizing and computerizing diagnosis: out of the CMI grew the (...) Medical Data Screen (MDS), one of the first computerized methods of deriving diagnoses from patient data. This essay follows the life course of these tools through the second half of the twentieth century. It argues for a genealogy of biomedical computing and computerized diagnosis that takes more seriously the continuities between computer-based digital practices and paper-based analog ones. The computerized MDS evolved from, and rested upon, paper data practices associated with questionnaires and surveys. The interlocking histories of the CMI and the MDS prompt a reconsideration of the material and temporal parameters within which the history of computerized medicine has conventionally been understood. (shrink)

Not long ago I visited the Personal Genome Project's website. The PGP describes its mission as “creating public genome, health, and trait data.” In the “Participant Profiles” section, I found several entries that disclosed the names of individuals along with their date of birth, sex, weight, height, blood type, race, health conditions, medications, allergies, medical procedures, and more. Other profiles did not feature names but provided all of the other details. I had no special access to this information. (...) It is available to absolutely anyone with Internet access. The PGP is part of a trend known as “open data.” Many government and private entities have launched initiatives to compile very large data resources and to make them available to the public. President Obama himself has endorsed open data by issuing a May 2013 executive order directing that, to the extent permitted by law, the federal government must release its data to the public in forms that make it easy to locate, access, and use. (shrink)

The Data Protection Act 1998 presents a number of significant challenges to data controllers in the health sector. To assist data controllers in understanding their obligations under the act, the Information Commissioner has published guidance, The Use and Disclosure of Health Data, which is reproduced here. The guidance deals, among other things, with the steps that must be taken to obtain patient data fairly, the implied requirements of the act to use anonymised or psuedonymised (...) class='Hi'>data where possible, an exemption applicable principally to records based research, the right of patients to object to the processing of their data, and the interface of the act and the common law duty of confidence. (shrink)

BackgroundThe SARS-CoV-2 pandemic has highlighted once more the great need for comprehensive access to, and uncomplicated use of, pre-existing patient data for medical research. Enabling secondary research-use of patient-data is a prerequisite for the efficient and sustainable promotion of translation and personalisation in medicine, and for the advancement of public-health. However, balancing the legitimate interests of scientists in broad and unrestricted data-access and the demand for individual autonomy, privacy and social justice is a great challenge for (...) patient-based medical research.MethodsWe therefore conducted two questionnaire-based surveys among North-German outpatients (n = 650) to determine their attitude towards data-donation for medical research, implemented as an opt-out-process.ResultsWe observed a high level of acceptance (75.0%), the most powerful predictor of a positive attitude towards data-donation was the conviction that every citizen has a duty to contribute to the improvement of medical research (> 80% of participants approving data-donation). Interestingly, patients distinguished sharply between research inside and outside the EU, despite a general awareness that universities and public research institutions cooperate with commercial companies, willingness to allow use of donated data by the latter was very low (7.1% to 29.1%, depending upon location of company). The most popular measures among interviewees to counteract reservations against commercial data-use were regulation by law (61.4%), stipulating in the process that data are not sold or resold (84.6%). A majority requested control of both the use (46.8%) and the protection (41.5%) of the data by independent bodies.ConclusionsIn conclusion, data-donation for medical research, implemented as a combination of legal entitlement and easy-to-exercise-right to opt-out, was found to be widely supported by German patients and therefore warrants further consideration for a transposition into national law. (shrink)

The analysis of survival data often aims at the prediction of failure time distribution. In cases of competing risk events, the time distributions of more than one failure are under investigation. In this paper, the comparison of two approaches to analyzing survival data with competing risks is presented. The analyses are performed by use of an ensemble of dipolar trees with and without adjustment to competing risks.

Most people are completely oblivious to the danger that their medical data undergoes as soon as it goes out into the burgeoning world of big data. Medical data is financially valuable, and your sensitive data may be shared or sold by doctors, hospitals, clinical laboratories, and pharmacies—without your knowledge or consent. Medical data can also be found in your browsing history, the smartphone applications you use, data from wearables, your shopping list, (...) and more. At best, data about your health might end up in the hands of researchers on whose good will we depend to avoid abuses of power.2 Most likely, it will end up with data brokers who might sell it to a future employer, or an insurance company, or the government. At worst, your medical data may end up in the hands of criminals eager to commit extortion or identity theft. In addition to data harms related to exposure and discrimination, the collection of sensitive data by powerful corporations risks the creation of data monopolies that can dominate and condition access to health care. -/- This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define privacy as having one’s personal information and one’s personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.). (shrink)

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. (shrink)

Medical research data is sensitive personal data that needs to be protected from unauthorized access and unintentional disclosure. In a research setting, sharing of data within the scientific community is necessary in order to make progress and maximize scientific benefits derived from valuable and costly data. At the same time, convincingly protecting the privacy of people participating in medical research is a prerequisite for maintaining trust and willingness to share. In this commentary, we will (...) address this issue and the pitfalls involved in the context of the PEP project1 that provides the infrastructure for the Personalized Parkinson’s Project,2 a large cohort study on Parkinson’s disease from Radboud University Medical Center, in cooperation with Verily life Sciences, an Alphabet subsidiary. (shrink)

Medicine is often thought of as a science of the body, but it is also a science of data. In some contexts, it can even be asserted that data drive health. This article focuses on a key piece of data technology central to contemporary practices of medicine: the medical record. By situating the medical record in the perspective of its history, we inquire into how the kinds of data that are kept at sites of (...) clinical encounter often depend on informational requirements that originate well outside of the clinic, in particular in health insurance records systems. Although this dependency of today's electronic medical records on billing requirements is widely lamented by clinical providers, its history remains little studied. Following the archaeology of medicine developed by Michel Foucault in The Birth of the Clinic and expanding his methodology in light of more recent contributions to the field of media archaeology, this article excavates some of the underexplored technological conditions that help constitute today's electronic medical record. If in some contexts, it is true that data drive health, then an archaeology of medical records helps reveal how health insurance records often impact clinical care and, by extension, health and disease. (shrink)

Medical-legal partnerships connect legal advocates to healthcare providers and settings. Maintaining effectiveness of medical-legal partnerships and consistently identifying opportunities for innovation and adaptation takes intentionality and effort. In this paper, we discuss ways in which our use of data and quality improvement methods have facilitated advocacy at both patient (client) and population levels as we collectively pursue better, more equitable outcomes.

Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of (...) this "massive data" seems to be essential. This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature. Defines Big Data applications in health Presents the ethical value of the medical datasphere via the description of a model of an ethical analysis of Big Data Provides the recommendations and steps necessary for successful management and governance of personal health data Helps readers determine what conditions are essential for the development of the study of Big Data. (shrink)

A powerful movement is afoot to create a national computerized system of health records. Advocates claim it could save the health delivery system billions of dollars and improve the quality of health services. According to Lawrence Gostin, a leading commentator on privacy and health records, this new infrastructure is “already under way and [has] an aura of inevitability.” When it is in place, almost any information that is viewed as relevant to a decision in the health care delivery system would (...) be available to a large and yet undetermined number of individuals. The transformation of the collection and communication of health information from texts housed by health care providers and facilities to data electronically transmitted through networks of linked computers has significant implications for confidentiality and for data collection in scientific research. The best evidence clearly indicates that most people in the United States consider confidentiality for health information important and worry that the increased computerization of health records will result in inappropriate disclosure. (shrink)

A powerful movement is afoot to create a national computerized system of health records. Advocates claim it could save the health delivery system billions of dollars and improve the quality of health services. According to Lawrence Gostin, a leading commentator on privacy and health records, this new infrastructure is “already under way and [has] an aura of inevitability.” When it is in place, almost any information that is viewed as relevant to a decision in the health care delivery system would (...) be available to a large and yet undetermined number of individuals. The transformation of the collection and communication of health information from texts housed by health care providers and facilities to data electronically transmitted through networks of linked computers has significant implications for confidentiality and for data collection in scientific research. The best evidence clearly indicates that most people in the United States consider confidentiality for health information important and worry that the increased computerization of health records will result in inappropriate disclosure. (shrink)

Due to advancements in technology such as data science and artificial intelligence, healthcare research has gained momentum and is generating new findings and predictions on abnormalities leading to the diagnosis of diseases or disorders in human beings. On one hand, the extensive application of data science to healthcare research is progressing faster, while on the other hand, the ethical concerns and adjoining risks and legal hurdles those data scientists may face in the future slow down the progression (...) of healthcare research. Simply put, the application of data science to ethically guided healthcare research appears to be a dream come true. Hence, in this paper, we discuss the current practices, challenges, and limitations of the data collection process during medical image analysis (MIA) conducted as part of healthcare research and propose an ethical data collection framework to guide data scientists to address the possible ethical concerns before commencing data analytics over a medical dataset. (shrink)

Background: Scientific research entails systematic investigation. Publishing the findings of research in peer reviewed journals implies a high level of confidence by the authors in the veracity of their interpretation. Therefore it stands to reason that researchers should be prepared to share their raw data with other researchers, so that others may enjoy the same level of confidence in the findings. Method: In a prospective study, 29 corresponding authors of original research articles in a medical journal (the British (...) Medical Journal) were contacted to ascertain their preparedness to share the data from their research. The email contact was in one of two forms, a general request and a specific request. The type of request a researcher received was randomly allocated. Findings: Researchers receiving specific requests for data were less likely, and slower, to respond than researchers receiving general requests. Only one researcher released data. Most researchers were reluctant to release their data. Some required further information, clarification, or authorship. Interpretation: The general reluctance of researchers to consider requests for their data is of concern. It raises questions about the level of confidence that should be placed on their interpretations of the data. It also highlights an unfortunate situation where researchers are more concerned with losing an advantage than advancing science. (shrink)

BackgroundUse of routinely collected patient data for research and service planning is an explicit policy of the UK National Health Service and UK government. Much clinical information is recorded in free-text letters, reports and notes. These text data are generally lost to research, due to the increased privacy risk compared with structured data. We conducted a citizens’ jury which asked members of the public whether their medical free-text data should be shared for research for public (...) benefit, to inform an ethical policy.MethodsEighteen citizens took part over 3 days. Jurors heard a range of expert presentations as well as arguments for and against sharing free text, and then questioned presenters and deliberated together. They answered a questionnaire on whether and how free text should be shared for research, gave reasons for and against sharing and suggestions for alleviating their concerns.ResultsJurors were in favour of sharing medical data and agreed this would benefit health research, but were more cautious about sharing free-text than structured data. They preferred processing of free text where a computer extracted information at scale. Their concerns were lack of transparency in uses of data, and privacy risks. They suggested keeping patients informed about uses of their data, and giving clear pathways to opt out of data sharing.ConclusionsInformed citizens suggested a transparent culture of research for the public benefit, and continuous improvement of technology to protect patient privacy, to mitigate their concerns regarding privacy risks of using patient text data. (shrink)

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. 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.

All scientific activity involves some method of observation and some method of recording what is observed. These activities can be carried out in ways that involve little interaction between subject and object, as is the case when a telescope observes a far-away star. At the other end of the scale are experiments in modern high energy physics in which there is little distinction between the observer and the observed, and the process of observation materially affects the data that are (...) recorded. In this regard, research on human phenomena resembles modern physics more than it does classical astronomy.Research on human phenomena, however, differs from modern physics in the way in which it affects that which is observed. Both the procedures and the findings of research on human phenomena alter the modes of thinking and the self-awareness of the objects of study. (shrink)

All scientific activity involves some method of observation and some method of recording what is observed. These activities can be carried out in ways that involve little interaction between subject and object, as is the case when a telescope observes a far-away star. At the other end of the scale are experiments in modern high energy physics in which there is little distinction between the observer and the observed, and the process of observation materially affects the data that are (...) recorded. In this regard, research on human phenomena resembles modern physics more than it does classical astronomy.Research on human phenomena, however, differs from modern physics in the way in which it affects that which is observed. Both the procedures and the findings of research on human phenomena alter the modes of thinking and the self-awareness of the objects of study. (shrink)

In this paper, we explore the perspectives of expert stakeholders about who owns data in a medical information commons 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.

ABSTRACT In ethics, the use of empirical data has become more and more popular, leading to a distinct form of applied ethics, namely empirical ethics. This ‘empirical turn’ is especially visible in bioethics. There are various ways of combining empirical research and ethical reflection. In this paper we discuss the use of empirical data in a special form of Reflective Equilibrium (RE), namely the Network Model with Third Person Moral Experiences. In this model, the empirical data consist (...) of the moral experiences of people in a practice. Although inclusion of these moral experiences in this specific model of RE can be well defended, their use in the application of the model still raises important questions. What precisely are moral experiences? How to determine relevance of experiences, in other words: should there be a selection of the moral experiences that are eventually used in the RE? How much weight should the empirical data have in the RE? And the key question: can the use of RE by empirical ethicists really produce answers to practical moral questions? In this paper we start to answer the above questions by giving examples taken from our research project on understanding the norm of informed consent in the field of pediatric oncology. We especially emphasize that incorporation of empirical data in a network model can reduce the risk of self‐justification and bias and can increase the credibility of the RE reached. (shrink)

Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. (...) Yet, most biomedical researchers working outside of clinics and hospitals are denied access to patient records when, at the same time, clinicians who guard the patient data are not optimally prepared for the data’s analysis. Medical data collection is a time- and cost-intensive process that is most of all tedious, with few elements of intellectual and emotional satisfaction on its own. In this process, clinicians and bioinformaticians, each group with their own interests, have to join forces with the goal to generate medical data sets both from clinical trials and from routinely collected electronic health records that are, as much as possible, free from errors and obvious inconsistencies. The data cleansing effort as we have learned during curation of Singaporean clinical trial data is not a trivial task. The introduction of omics and sophisticated imaging modalities into clinical practice that are only partially interpreted in terms of diagnosis and therapy with today’s level of knowledge warrant the creation of clinical databases with full patient history. This opens up opportunities for re-analyses and cross-trial studies at future time points with more sophisticated analyses of the same data, the collection of which is very expensive. (shrink)

In Roper v. Simmons (2005) the United States Supreme Court announced a paradigm shift in jurisprudence. Drawing specifically on mounting scientific evidence that adolescents are qualitatively different from adults in their decision-making capacities, the Supreme Court recognized that adolescents are not adults in all but age. The Court concluded that the overwhelming weight of the psychological and neurophysiological data regarding brain maturation supports the conclusion that adolescents are qualitatively different types of agents than adult persons. The Supreme Court further (...) solidified its position regarding adolescents as less than fully mature and responsible decisionmakers in Graham v. Florida (2010) and Miller v. Alabama (2012). In each case, the Court concluded that the scientific evidence does not support the conclusion that children under 18 years of age possess adult capacities for personal agency, rationality, and mature choice. This study explores the implications of the Supreme Court decisions in Roper v. Simmons, Graham v. Florida, and Miller v. Alabama for the “mature minor” standard for medical decision making. It argues that the Supreme Court’s holdings in Roper, Graham, and Miller require no less than a radical reassessment of how healthcare institutions, courts of law, and public policy are obliged to regard minors as medical decisionmakers. The “mature minor” standard for medical decision making must be abandoned. (shrink)

In ethics, the use of empirical data has become more and more popular, leading to a distinct form of applied ethics, namely empirical ethics. This ‘empirical turn’ is especially visible in bioethics. There are various ways of combining empirical research and ethical reflection. In this paper we discuss the use of empirical data in a special form of Reflective Equilibrium (RE), namely the Network Model with Third Person Moral Experiences. In this model, the empirical data consist of (...) the moral experiences of people in a practice. Although inclusion of these moral experiences in this specific model of RE can be well defended, their use in the application of the model still raises important questions. What precisely are moral experiences? How to determine relevance of experiences, in other words: should there be a selection of the moral experiences that are eventually used in the RE? How much weight should the empirical data have in the RE? And the key question: can the use of RE by empirical ethicists really produce answers to practical moral questions?In this paper we start to answer the above questions by giving examples taken from our research project on understanding the norm of informed consent in the field of pediatric oncology. We especially emphasize that incorporation of empirical data in a network model can reduce the risk of self‐justification and bias and can increase the credibility of the RE reached. (shrink)

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

Self-reported data are regarded by medical researchers as invalid and less reliable than data produced by experts in clinical settings, yet individuals can increasingly contribute personal information to medical research through a variety of online platforms. In this article we examine this ‘participatory turn’ in healthcare research, which claims to challenge conventional delineations of what is valid and reliable for medical practice, by using aggregated self-reported experiences from patients and ‘pre-patients’ via the internet. We focus (...) on 23andMe, a genetic testing company that collects genetic material and self-reported information about disease from its customers. Integral to this research method are relations of trust embedded in the information exchange: trust in customers’ data; trust between researchers/company and research subjects; trust in genetics; trust in the machine. We examine the performative dimension of these trust relations, drawing on Shapin and Schaffer’s discussion of how material, literary and social technologies are used in research in order to establish trust. Our scepticism of the company’s motives for building trust with the self-reporting consumer forces us to consider our own motives. How does the use of customer data for research purposes by 23andMe differ from the research practices of social scientists, especially those who also study digital traces? By interrogating the use of self-reported data in the genetic testing context, we examine our ethical responsibilities in studying the digital selves of others using internet methods. How researchers trust data, how participants trust researchers, and how technologies are trusted are all important considerations in studying the social life of digital data. (shrink)

This paper argues that assessing personal responsibility in healthcare settings for the allocation of medical resources would be too privacy-invasive to be morally justifiable. In addition to being an inappropriate and moralizing intrusion into the private lives of patients, it would put patients’ sensitive data at risk, making data subjects vulnerable to a variety of privacy-related harms. Even though we allow privacy-invasive investigations to take place in legal trials, the justice and healthcare systems are not analogous. The (...) duty of doctors and healthcare professionals is to help patients as best they can—not to judge them. Patients should not be forced into giving up any more personal information than what is strictly necessary to receive an adequate treatment, and their medical data should only be used for appropriate purposes. Medical ethics codes should reflect these data rights. When a doctor asks personal questions that are irrelevant to diagnose or treat a patient, the appropriate response from the patient is: ‘none of your business’. (shrink)

BackgroundIt has been argued that ethics review committees—e.g., Research Ethics Committees, Institutional Review Boards, etc.— have weaknesses in reviewing big data and artificial intelligence research. For instance, they may, due to the novelty of the area, lack the relevant expertise for judging collective risks and benefits of such research, or they may exempt it from review in instances involving de-identified data.Main bodyFocusing on the example of medical research databases we highlight here ethical issues around de-identified data (...) sharing which motivate the need for review where oversight by ethics committees is weak. Though some argue for ethics committee reform to overcome these weaknesses, it is unclear whether or when that will happen. Hence, we argue that ethical review can be done by data access committees, since they have de facto purview of big data and artificial intelligence projects, relevant technical expertise and governance knowledge, and already take on some functions of ethical review. That said, like ethics committees, they may have functional weaknesses in their review capabilities. To strengthen that function, data access committees must think clearly about the kinds of ethical expertise, both professional and lay, that they draw upon to support their work.ConclusionData access committees can undertake ethical review of medical research databases provided they enhance that review function through professional and lay ethical expertise. (shrink)

In “Materialized Oppression in Medical Tools and Technologies”, we show how oppression can be inscribed in medical devices. We consider oximeters and spirometers, drawing heavily on the work of anthropologist Amy Moran-Thomas and historian Lundy Braun. Both devices encode racial biases: oximeters because they do not correct for race, and spirometers because they do. We zoom out from these particular devices to examine a wide range of tools and technologies, and we build a theoretical framework that covers not (...) only race but other axes of oppression: gender, ability, class, etc. -/- We are inspired by the peer commentaries and the guest editorial, and we are grateful to the contributors for engaging with our work and sharing their expertise. Their responses to our target article advance our understanding in three directions. First, a set of responses contextualize our framework within the histories of medicine, bioethics, and science and technology studies. Second, a set of responses provide additional examples of oppressive medical tools and technologies: not only material artifacts like oximeters and spirometers, but also spatial environments like inpatient psychiatric units, and cognitive niches comprised of digital technologies like electronic medical records and medical data classifications. Third, a set of responses propose countermeasures: operationalizing anti-oppressive practices for device design and testing; mapping the complex causal relationships involved in real-world problems; and repairing the harms done by materialized oppression through racism-conscious praxis. We reply to these three sets of responses—respectively, the “past, present, and future” of oppressive medical objects and spaces—in what follows. (shrink)

As the capacity to generate, store, aggregate and combine ever greater volumes and types of data about individuals, behaviours and interactions continues to expand apace, so too does the challenge of ensuring suitable and appropriate governance of that data. In broad terms, the challenge is simple; how to ensure the benefits of data, such as improvements in service delivery or individual and collective well-being, while avoiding harms such as discrimination, injustice or placing undue burdens on individuals and (...) groups. The difficulty, as ever, lies in the details. As Nicol et al 1 observe, informed consent is important but does not replace the need for an appropriate governance framework that covers the wider ecosystem of data generation, use and reuse. Moreover, there is no one universal oversight mechanism for data sharing, but what counts as ‘appropriate’ must take into account the context and purpose of data use. In this regard, the study by Ford et al is an important and valuable contribution to our understanding of the …. (shrink)

EMBARGOED - expected end date 06.06.2025.