Ethics in Health Informatics.

Contemporary bioethics was fledged and is sustained by challenges posed by new technologies. These technologies have affected many lives. Yet health informatics affects more lives than any of them. The challenges include the development and the appropriate uses and users of machine learning software, the balancing of privacy rights against the needs of public health and clinical practice in a time of Big Data analytics, whether and how to use this technology, and the role of ethics and standards in health policy. Historical antecedents in statistics and evidence-based practice foreshadow some of the difficulties now faced, but the scope and scale of these challenges requires that ethics, too, be brought to scale in parallel, especially given the size of contemporary data sets and the processing power of new computers. Fortunately, applied ethics affords a variety of tools to help identify and rank applicable values, support best practices, and contribute to standards. The bioethics community can in partnership with the informatics community arrive at policies that promote the health sciences while reaffirming the many and varied rights that patients expect will be honored.

The Belmont Report and Innovative Clinical Research.

A central pillar of the Belmont Report is that a bright line must be drawn between medical practice and biomedical research. That line may have been brighter 50 years ago. Today, the typical physician is likely to work for a corporation or health system that styles itself as a learning health system. Such systems increasingly emphasize the (research-like) use of data to measure quality, encourage efficiency, ensure safety, and guide a standardized approach to clinical care. While these activities are not considered research, they pose many of the same risks or conflicts of loyalty. In research, the doctor's fiduciary loyalty to the patient is compromised by a loyalty to the scientific process. In learning health systems, the doctor's loyalty is compromised by loyalty to the system and its metrics. In this world, it is not clear that research-as conceptualized by the Belmont Report, codified in the Common Rule, and overseen by IRBs-is a uniquely risky activity deserving of such uniquely strict oversight. Perhaps, instead, the divided loyalties and conflicts of interest faced by everyday clinicians working in learning health systems demand a protective framework similar to the one that we now have for the activities that we designate as "research." This article compares the risks of the various activities that might be called "research" and suggests a unified system of oversight for all of them.

Research versus practice: The dilemmas of research ethics in the era of learning health-care systems.

In this article we attempt to answer the question of how the ethical and conceptual framework (ECF) for a learning health-care system (LHS) affects some of the main controversies in research ethics by addressing five key problems of research ethics: (a) What is the difference between practice and research? (b) What is the relationship between research ethics and clinical ethics? (c) What is the ethical relevance of the principle of clinical equipoise? (d) Does participation in research require a higher standard of informed consent than the practice of medicine? and (e) What ethical principle should take precedence in medicine? These questions allow us to construct two opposite idealized positions on the distinction between research and practice: the integration model and the segregation model of research and practice. We then compare the ECF for an LHS with these two idealized positions. We argue that the ECF for a LHS does not, in fact, solve these problems, but that it is a third, separate position in the relationship between research ethics and clinical ethics. Moreover, we suggest that the ECF for a LHS raises new ethical problems that require additional ethical analysis and justification. Our article contributes to the discussion on the relationship between research ethics and clinical ethics, revealing that although a learning health-care system may significantly change the landscape of health care, some ethical dilemmas still require resolving on both theoretical and policy-making levels.

Learning health systems, clinical equipoise and the ethics of response adaptive randomisation.

To give substance to the rhetoric of 'learning health systems', a variety of novel trial designs are being explored to more seamlessly integrate research with medical practice, reduce study duration and reduce the number of participants allocated to ineffective interventions. Many of these designs rely on response adaptive randomisation (RAR). However, critics charge that RAR is unethical on the grounds that it violates the principle of equipoise. In this paper, I reconstruct critiques of RAR as holding that it is inconsistent with five important ethical principles. I then argue that these criticisms rest on a faulty view of equipoise encouraged by the idea that a RAR study models the beliefs of a single rational agent about the relative merits of the interventions being studied. I outline a view in which RAR models an idealised health system in which diverse communities of fully informed experts shrink or grow as their constituent members update their expert opinions in light of reliable medical evidence. I show how a proper understanding of clinical equipoise can reconcile this conception of RAR with these five ethical principles. This analysis removes an in-principle objection to RAR and sheds important light on the relationship between clinical equipoise and transient diversity in the scientific community.