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Artificially intelligent systems (AISs) are being created by software developing companies (SDCs) to influence clinical decision‐making. Historically, clinicians have led healthcare decision‐making, and the introduction of AISs makes SDCs novel actors in the clinical decision‐making space. Although these AISs are intended to influence a clinician's decision‐making, SDCs have been clear that clinicians are in fact the final decision‐makers in clinical care, and that AISs can only inform their decisions. As such, the default position is that clinicians should hold responsibility for the outcomes of the use of AISs. This is not the case when an AIS has influenced a clinician's judgement and their subsequent decision. In this paper, we argue that this is an imbalanced and unjust position, and that careful thought needs to go into how personal moral responsibility for the use of AISs in clinical decision‐making should be attributed. This paper employs and examines the difference between prospective and retrospective responsibility and considers foreseeability as key in determining how personal moral responsibility can be justly attributed. This leads us to the view that moral responsibility for the outcomes of using AISs in healthcare ought to be shared by the clinical users and SDCs. [ABSTRACT FROM AUTHOR]

A recent study published in Human Reproduction investigated the feasibility of testicular tissue re-implantation as a method to restore fertility in infertile adult males. The study found that while the transplanted tissue was able to survive, it did not generate sperm, raising questions about the suitability of this approach for certain patients. The study also emphasized the importance of publishing negative results to inform future clinical applications. The authors discussed the ethical considerations of "first in human" procedures and the benefits of publishing negative results. They concluded that re-implantation of testicular tissue may be more beneficial for prepubertal boys facing gonadotoxic therapies, as their testicular tissue is healthier and more likely to generate functional sperm. [Extracted from the article]

Auditory Verbal Hallucinations (AVH) are a hallmark of psychosis, but affect many other clinical populations. Patients' understanding and self-management of AVH may differ between diagnostic groups, change over time, and influence clinical outcomes. This study aimed to explore patients' understanding and self-management of AVH in a young adult clinical population. 35 participants were purposively sampled from a youth mental health service. Participants completed diary and photo-elicitation tasks, and the resulting materials were discussed at in-depth interviews. Themes were derived using conventional content analysis. Three themes emerged. (1) Searching for answers, forming identities – voice-hearers sought to explain their experiences, resulting in the construction of identities for voices and themselves. Explanations were drawn from participants' life-stories and belief-systems. (2) Coping goals – patients' self-management strategies were diverse, reflecting the diverse negative experience of AVH, e.g. as distressing sounds, overwhelming emotions, or as threats to agency. (3) Outlook – participants formed an overall outlook on their life with AVH. Resignation and hopelessness in connection with disabling AVH are contrasted with "acceptance" or integration, described as positive, ideal, or mature. Trans-diagnostic commonalities in understanding and self-management of AVH are highlighted. These offer targets for individual therapies and further research. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To explore moral distress empirically and conceptually, to understand the factors that mitigate and exacerbate moral distress and construct a model that represents how moral distress relates to its constituent parts and related concepts. Background: There is ongoing debate about how to understand and respond to moral distress in nursing practice. Design: The overarching design was feminist empirical bioethics in which feminist interpretive phenomenology provided the tools for data collection and analysis, reported following the COREQ guidelines. Using reflexive balancing, the empirical data were combined with feminist theory to produce normative recommendations about how to respond to moral distress. The Moral Distress Model presented in this paper is a culmination of the empirical data and theory. Methods: Using feminist interpretive phenomenology, critical care nurses in the United Kingdom (n = 21) were interviewed and data analysed. Reflexive Balancing was used to integrate the data with feminist theory to provide normative recommendations about how to understand moral distress. Results: There are five compounding factors that exacerbate/ mitigate nurses' experiences of moral distress: epistemic injustice; the roster lottery; conflict between one's professional and personal responsibilities; ability to advocate and team dynamics. In addition to the causal connection and responses to moral distress, these factors make up the moral distress model which can guide approaches to mitigate moral distress. Conclusions: The Moral Distress Model is the culmination of these data and theorising formulated into a construct to explain how each element interacts. We propose that this model can be used to inform the design of interventions to address moral distress. [ABSTRACT FROM AUTHOR]

Background: The final year of life is often associated with increasing health complexities and use of health services. This frequently includes admission to an acute hospital which may or may not convey overall benefit. This uncertainty makes decisions regarding admission complex for clinicians. There is evidence of much variation in approaches to admission., Aims: To explore how Primary Care clinicians approach hospitalisation decisions for people in the final year of life., Design: Systematic literature review and narrative synthesis., Data Sources: We searched the following databases from inception to April 2023: CINAHL, Cochrane Library, Embase, MedLine, PsychInfo and Web of Science followed by reference and forward citation reviews of included records., Results: A total of 18 studies were included: 14 qualitative, 3 quantitative and 1 mixed methods study. As most of the results were qualitative, we performed a thematic analysis with narrative synthesis. Six key themes were identified: navigating the views of other stakeholders; clinician attributes; clinician interpretation of events; the perceived adequacy of the current setting and the alternatives; system factors and continuity of care., Conclusion: This review shows that a breadth of factors influence hospitalisation decisions. The views of other stakeholders take great importance but it is not clear how these views are, or should be, should be balanced. Clinician factors, such as experience with palliative care and clinical judgement, are also important. Future research should focus on how different aspects of the decision are balanced and to consider if, and how, this could be improved to optimise patient-centred outcomes and use of health resources., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

With the introduction of artificial intelligence (AI) to healthcare, there is also a need for professional guidance to support its use. New (2022) reports from National Health Service AI Lab & Health Education England focus on healthcare workers' understanding and confidence in AI clinical decision support systems (AI-CDDSs), and are concerned with developing trust in, and the trustworthiness of these systems. While they offer guidance to aid developers and purchasers of such systems, they offer little specific guidance for the clinical users who will be required to use them in patient care.This paper argues that clinical, professional and reputational safety will be risked if this deficit of professional guidance for clinical users of AI-CDDSs is not redressed. We argue it is not enough to develop training for clinical users without first establishing professional guidance regarding the rights and expectations of clinical users.We conclude with a call to action for clinical regulators: to unite to draft guidance for users of AI-CDDS that helps manage clinical, professional and reputational risks. We further suggest that this exercise offers an opportunity to address fundamental issues in the use of AI-CDDSs; regarding, for example, the fair burden of responsibility for outcomes., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)