Cambridge Quarterly of Healthcare Ethics最新文献

The day has arrived that genetic tests for educational outcomes are available to the public. Today parents and students alike can send off a sample of blood or saliva and receive a 'genetic report' for a range of characteristics relevant to education, including intelligence, math ability, reading ability, and educational attainment. DTC availability is compounded by a growing "precision education" initiative, which proposes the application of DNA tests in schools to tailor educational curricula to children's genomic profiles. Here I argue that these happenings are a strong signal of the geneticization of education; the process by which educational abilities and outcomes come to be examined, understood, explained, and treated as primarily genetic characteristics. I clarify what it means to geneticize education, highlight the nature and limitations of the underlying science, explore both real and potential downstream bioethical implications, and make proposals for mitigating negative impacts.

Many bioethicists have recently shifted from using "physician-assisted suicide" (PAS) to "medical aid-in-dying" (MAID) to refer to the act of voluntarily hastening one's death with the assistance of a medical provider. This shift was made to obscure the practice's connection to "suicide." However, as the charge of "suicide" is fundamental to arguments against the practice, "MAID" can only be used by its proponents. The result has been the fragmentation of the bioethical debate. By highlighting the role of human agency-as opposed to natural processes-in causing death, the term "PAS" makes it easier both to perceive potential risks to vulnerable populations and to affirm suicide as a potentially autonomous choice. As such, "PAS" thus more transparently expresses the arguments of both supporters and opponents of the "right to die," while avoiding the unnecessary stigmatization of suicide and suicidal people which is a result of the usage of "MAID."

Whether anorexic patients should be able to refuse treatment when this refusal potentially has a fatal outcome is a vexed topic. A recent proposal for a new category of "terminal anorexia" suggests criteria when a move to palliative care or even physician-assisted suicide might be justified. The author argues that this proposed diagnosis presents a false sense of certainty of the illness trajectory by conceptualizing anorexia in analogy with physical disorders and stressing the effects of starvation. Furthermore, this conceptualization is in conflict with the claim that individuals who meet the diagnostic criteria for terminal anorexia have decision-making capacity. It should therefore be rejected.

Major depressive disorder is one of the most common serious illnesses worldwide; the disease is also among those with the lowest rates of treatment. Barriers to access to care, both practical and psychological, contribute significantly to these low treatment rates. Among such barriers are regulations in many nations that require a physician's prescription for most pharmacological treatments including selective serotonin reuptake inhibitors (SSRIs). These rules are designed to protect patients. However, such regulations involve a tradeoff between the welfare of "visible" victims, who might suffer negative consequences from a lack of regulation, and the well-being of invisible "victims," who likely experience negative consequences that result from increased barriers to care. This article explores these tradeoffs and argues in favor of shifting SSRIs from prescription-only to over-the-counter status.

The Interprofessional Education Collaborative’s (IPEC’s) core competencies are accreditation standards of most, if not all, healthcare professions (Interprofessional Education Collaborative Expert Panel [2016, Core Competencies for Interprofessional Collaborative Practice: 2016 Update. Washington, DC: IPEC]). Limited literature exists on interprofessional (IP) learning outcomes in healthcare ethics; even fewer studies include debrief sessions. Interprofessional education (IPE) case discussion using web-based technology is a promising way to incorporate ethics content. This article summarizes a model for healthcare programs to create, conduct, and assess synchronous IPE ethics discussions and debrief sessions. Specifically, this article highlights debrief sessions that followed a standardized patient (SP) IP interaction with students from pharmacy and advanced practice nursing. Qualitative analysis of debrief comments identified four themes: the benefit of IP collaboration, the importance of patient-centered care, the need to adapt clinical recommendations with ethical challenges, and the importance of trust among team members. The findings indicate web-based, synchronous IP/SP ethics simulations and debrief sessions are an effective, albeit laborious, method for collaboration and reflection.

Animal experimentation raises value conflicts between animal protection and other goods, such as freedom of inquiry or health and safety. If governments can phase out the practice by non-prohibitive incentive-setting, the pro tanto moral rationale for doing so is obvious. So why should they not? This article first sketches a fictional scenario in which a government adopts a phase-out plan for animal experimentation. It then considers two moral objections to this plan: First, the plan unduly restricts freedom of inquiry, and second, it merely displaces animal experimentation across borders and thus fails to reduce animal suffering. Both arguments are refined premise by premise to articulate their strongest versions. The two objections can help to narrow down desiderata for good phase-out plans. However, they do not provide a compelling case against phase-out planning as such because they miss its incremental and constructive nature. Unless better arguments can be provided, it appears that government inaction on phasing out animal experimentation lacks moral justification.

Xenotransplant patient selection recommendations restrict clinical trial participation to seriously ill patients for whom alternative therapies are unavailable or who will likely die while waiting for an allotransplant. Despite a scholarly consensus that this is advisable, we propose to examine this restriction. We offer three lines of criticism: (1) The risk-benefit calculation may well be unfavorable for seriously ill patients and society; (2) the guidelines conflict with criteria for equitable patient selection; and (3) the selection of seriously ill patients may compromise informed consent. We conclude by highlighting how the current guidance reveals a tension between the societal values of justice and beneficence.

Teaching ethics is crucial to health sciences education. Doing it well requires a willingness to engage contentious social issues. Those issues introduce conflict and risk, but avoiding them ignores moral diversity and renders the work of ethics education irrelevant. Therefore, when (not if) contentious issues and moral differences arise, they must be acknowledged and can be addressed with humility, collegiality, and openness to support learning. Faculty must risk moments when not everyone will "feel safe," so the candor implied in psychological safety can emerge. The deliberative and social work of ethics education involves generous listening, wading into difference, and wondering together if our beliefs and arguments are as sound as we once thought. By forecasting the need for candid engagement with contentious issues and moral difference, establishing ground rules, and bolstering due process structures for faculty and students, a riskier and more relevant ethics pedagogy can emerge. Doing so will prepare everyone for the moral diversity they can expect in our common life and in practice.

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are "black boxes." The initial response in the literature was a demand for "explainable AI." However, recently, several authors have suggested that making AI more explainable or "interpretable" is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a "lethal prejudice." In this paper, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to-and perhaps even harm-patients.

Position papers on artificial intelligence (AI) ethics are often framed as attempts to work out technical and regulatory strategies for attaining what is commonly called trustworthy AI. In such papers, the technical and regulatory strategies are frequently analyzed in detail, but the concept of trustworthy AI is not. As a result, it remains unclear. This paper lays out a variety of possible interpretations of the concept and concludes that none of them is appropriate. The central problem is that, by framing the ethics of AI in terms of trustworthiness, we reinforce unjustified anthropocentric assumptions that stand in the way of clear analysis. Furthermore, even if we insist on a purely epistemic interpretation of the concept, according to which trustworthiness just means measurable reliability, it turns out that the analysis will, nevertheless, suffer from a subtle form of anthropocentrism. The paper goes on to develop the concept of strange error, which serves both to sharpen the initial diagnosis of the inadequacy of trustworthy AI and to articulate the novel epistemological situation created by the use of AI. The paper concludes with a discussion of how strange error puts pressure on standard practices of assessing moral culpability, particularly in the context of medicine.