Along with potential benefits to healthcare delivery, machine learning healthcare applications raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure th...

ML-HCAs have the potential to significantly change an entire healthcare system. It is not even necessary to presume that this will be disruptive but sufficient to assume that the mere adaptation of...

Volume 19, Issue 6, June 2019, Page 19-21.

Clinicians and administrators have a professional obligation to contribute to improvement of healthcare quality. At the same time, participation in embedded research poses risks to healthcare institutions. Disclosure of an institution’s sensitive information could endanger relationships with patients and undermine its reputation. The existing ethical framework for learning healthcare systems does not address the conflict between the OTC and institutional interests. Ethical guidance and policy regulation are needed to create a safe environment for embedded research. In (...) this article we analyse the EF for LHSs and the concept of professionalism. We suggest that the EF should be supplemented with an obligation to protect provider’s legitimate interests. We define legitimate interests as those that enable providers to discharge their primary duties. We argue that both the OTC and the obligation to protect legitimate interests are grounded in the concept of medical professionalism and can be understood as a matter of contract between a democratic society and medical professionals. The proposed supplemented EF can be implemented into a regulatory system in three different ways: the self-regulating: where providers decide themselves how to balance the ethical claims, the centralised: where a governmental institution decides the right balance between providers’ interests and interests of a health system; and the mediating: where medical professionals, the state and patients negotiate their interests. Our article contributes to the discussion on ethical relevance of providers’ interests and the regulatory model for weighing opposite interests in LHSs. (shrink)

We explore how principles predicting the success of a medical information commons advantaged or disadvantaged three MIC initiatives in three Canadian provinces. Our MIC case examples demonstrate that practices and policies to promote access to and use of health information can help improve individual healthcare and inform a learning health system. MICs were constrained by heterogenous health information protection laws across jurisdictions and risk-averse institutional cultures. A networked approach to MICs would unlock even more potential for national and (...) international data collaborations to improve health and healthcare. (shrink)

Peer commentary on Char, Abràmoff & Feudtner (2020) target article: "Identifying Ethical Considerations for Machine Learning Healthcare Applications" .

The pipeline framework for identifying ethical issues in machine learning healthcare applications outlined by Char et al. is a very useful starting point for the systematic consideration...

The pipeline model framework proposed by Char et al. makes a timely contribution to the literature in allowing one to take a step back and consider machine learning healthcare app...

The field of artificial intelligence (AI) ethics has exploded in recent years, with countless academics, organizations, and influencers rushing to consider how AI technology can be developed and im...

Healthcare delivery is an interpersonal endeavor. In every clinical interaction, providers have an ethical obligation to show respect to their patients, and ideally over time these interactions lea...

This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the best (...) way to make progress in remedying these ethical problems is to distil their epistemic core and to identify appropriate epistemic norms as guardrails. The viability of this approach will be highlighted based on four key issues: trust, responsibility, paternalism, and fairness. In that respect, the paper also contributes to a more pronounced view of the specific methodological challenges for ethical theorizing, when the point of reference are complex statistical models that make predictions, rather than individual agents, acting for reasons. (shrink)

Char et al. describe an interesting and useful approach in their paper, “Identifying ethical considerations for machine learning healthcare applications.” Their proposed framework, which see...

The application of machine learning algorithms to healthcare data can enhance patient care while also reducing healthcare worker cognitive load. These algorithms can be used to detect anomalous physiological readings, potentially leading to expedited emergency response or new knowledge about the development of a health condition. However, while there has been much research conducted in assessing the performance of anomaly detection algorithms on well-known public datasets, there is less conceptual comparison across unsupervised and supervised performance on physiological (...) data. Moreover, while heart rate data are both ubiquitous and noninvasive, there has been little research specifically for anomaly detection of this type of data. Considering that heart rate data are indicative of both potential health complications and an individual’s physical activity, this is a rich source of largely overlooked data. To this end, we employed and evaluated five machine learning algorithms, two of which are unsupervised and the remaining three supervised, in their ability to detect anomalies in heart rate data. These algorithms were then evaluated on real heart rate data. Findings supported the effectiveness of local outlier factor and random forests algorithms in the task of heart rate anomaly detection, as each model generalized well from their training on simulated heart rate data to real world heart rate data. Furthermore, results support that simulated data can help configure algorithms to a degree of performance when real labeled data are not available and that this type of learning might be especially helpful in initial deployment of a system without prior data. (shrink)

Informed consent is at the core of the clinical relationship. With the introduction of machine learning in healthcare, the role of informed consent is challenged. This paper addresses the issue of whether patients must be informed about medical ML applications and asked for consent. It aims to expose the discrepancy between ethical and practical considerations, while arguing that this polarization is a false dichotomy: in reality, ethics is applied to specific contexts and situations. Bridging this gap and considering (...) the whole picture is essential for advancing the debate. In the light of the possible future developments of the situation and the technologies, as well as the benefits that informed consent for ML can bring to shared decision-making, the present analysis concludes that it is necessary to prepare the ground for a possible future requirement of informed consent for medical ML. (shrink)

While the use of archives is common as a research methodology in the history and philosophy of science, training in archival methods is more often encountered as part of graduate-level training than in the undergraduate curriculum. Because many HPS instructors are likely to have encountered archival methods during their own research training, they are uniquely positioned to make effective pedagogical use of archives in classes comprised of undergraduate science students. Further, because doing this may require changing the way HPS instructors (...) think about the aims and varieties of archival research, archivists themselves can be valuable resources in developing archives-based learning activities for science students in HPS classrooms. In this article, we describe an archives-based learning activity developed for a population of primarily pre-medical students in a healthcare ethics class and discuss the pedagogical benefits of this activity. This activity was developed via a collaboration between an HPS instructor and an education archivist. Our hope is that this discussion may serve both as a proof of concept for the use of archives-based learning activities as tools for teaching HPS to science students, and as an argument for the unique benefits that archival engagement can impart to science students. (shrink)

Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study Design and Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results: We find that concerns regarding (...) explainability are not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating unexplainable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine. Conclusion: Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems. (shrink)

The traditional organizational boundaries between healthcare, social work, police and other non-profit organizations are fading and being replaced by new relational patterns among a variety of disciplines. Professionals work from their own history, role, values and relationships. It is often unclear who is responsible for what because this new network structure requires rules and procedures to be re-interpreted and re-negotiated. A new moral climate needs to be developed, particularly in the early stages of integrated services. Who should do what, (...) with whom and why? Departing from a relational and hermeneutic perspective, this article shows that professionals in integrated service networks embark upon a moral learning process when starting to work together for the client’s benefit. In this context, instrumental ways of thinking about responsibilities are actually counterproductive. Instead, professionals need to find out who they are in relation to other professionals, what core values they share and what responsibilities derive from these aspects. This article demonstrates moral learning by examining the case of an integrated social service network. The network’s development and implementation were supported by responsive evaluation, enriched by insights of care ethics and hermeneutic ethics. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order (...) to investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

Caring is at the core of what nurses and other health professionals do. But caring encompasses more than simply looking after people's physical health needs. People requiring any health service will have psychological needs that affect their feelings, thoughts, and behaviour. Good psychological care can even help improve physical health outcomes. An Introduction to Psychological Care in Nursing and the Health Professions explains and promotes the importance of psychological care for people when they become physically ill, giving a sound theoretical (...) basis to ensure care is evidence-based. It encourages the reader to think about the effects of illness and disability on patients, and to understand what can be done to identify and minimise any difficulties they might be experiencing in these areas. The chapters cover: the meaning and elements of care and holistic care; a model of psychological care in practice; the personal qualities and skills of carers that best underpin psychological care delivery, and how these might be enhanced; the knowledge needed for effective psychological caregiving; psychological care as it might be practised in a range of health care settings. This text contains key learning points, practical activities, reflective exercises and case illustrations. It is ideal for student and practising nurses, and health professionals who would like to improve their care for patients in this essential area. (shrink)

Educating young people about how to interact with patients at the end of their lives is challenging. A qualitative study based on Husserl’s phenomenological approach was performed to describe the learning experience of secondary education students after watching, analysing, and reflecting on two videoclips featuring Cameron Duncan, a young man suffering from terminal cancer. Students from three vocational centres providing training in ancillary nursing, pharmacy, and dependent care in the Community of Madrid visited the Palliative Care Hospital. A total (...) of 110 students, with a median age of nineteen years, participated in the study. The students’ learning experience through the videoclips reveals the essential aspects required in palliative care, since the character suffers in every dimension of his being: physically as well as psychologically, socially, and spiritually. Therefore, this requires healthcare professionals to understand their experience, putting themselves in their place, to provide affectionate care and to display communication skills leading to a quality helping relationship. Four comprehensive educational categories were identified in the study. The students learned the importance of: 1) providing comprehensive and affectionate care to patients, 2) the need for communication skills in caring for patients, 3) being aware of the end of life and time left, 4) valuing life and fighting for what one wishes to attain. A visit to a PC hospital and the viewing of videoclips and reflection upon these, represents a useful strategy for secondary education healthcare students. The study indicates that videoclips are an innovative method for becoming aware of the various issues pertaining to palliative care. (shrink)

The basics -- The dignity of the human person -- Autonomy, consent, refusing treatment and boundaries -- Ethics and non-autonomous patients -- Confidentiality, privacy, data protection, truth telling and trust -- Ethical issues at the beginning of life -- Ethical issues about babies, children and young adults -- Ethical issues at the end of life -- Dying and death: ethical issues -- Loss, grief and bereavement, burn-out and the wounded healer -- Conscientious objection and loyalties.

The invisibility of nursing work has been discussed in the international literature but not in relation to learning clinical skills. Evans and Guile's (Practice‐based education: Perspectives and strategies, Rotterdam: Sense, 2012) theory of recontextualisation is used to explore the ways in which invisible or unplanned and unrecognised learning takes place as newly qualified nurses learn to delegate to and supervise the work of the healthcare assistant. In the British context, delegation and supervision are thought of as skills (...) which are learnt “on the job.” We suggest that learning “on‐the‐job” is the invisible construction of knowledge in clinical practice and that delegation is a particularly telling area of nursing practice which illustrates invisible learning. Using an ethnographic case study approach in three hospital sites in England from 2011 to 2014, we undertook participant observation, interviews with newly qualified nurses, ward managers and healthcare assistants. We discuss the invisible ways newly qualified nurses learn in the practice environment and present the invisible steps to learning which encompass the embodied, affective and social, as much as the cognitive components to learning. We argue that there is a need for greater understanding of the “invisible learning” which occurs as newly qualified nurses learn to delegate and supervise. (shrink)

Digitalisation of health and the use of health data in artificial intelligence, and machine learning (ML), including for applications that will then in turn be used in healthcare are major themes permeating current UK and other countries’ healthcare systems and policies. Obtaining rich and representative data is key for robust ML development, and UK health data sets are particularly attractive sources for this. However, ensuring that such research and development is in the public interest, produces public benefit (...) and preserves privacy are key challenges. Trusted research environments (TREs) are positioned as a way of balancing the diverging interests in healthcare data research with privacy and public benefit. Using TRE data to train ML models presents various challenges to the balance previously struck between these societal interests, which have hitherto not been discussed in the literature. These challenges include the possibility of personal data being disclosed in ML models, the dynamic nature of ML models and how public benefit may be (re)conceived in this context. For ML research to be facilitated using UK health data, TREs and others involved in the UK health data policy ecosystem need to be aware of these issues and work to address them in order to continue to ensure a ‘safe’ health and care data environment that truly serves the public. (shrink)

The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use (...) of affirmative algorithms; that is, algorithms trained on diverse datasets that perform better for traditionally disadvantaged groups. Whilst such algorithmic decisions may be unfair, the fairness of algorithmic decisions is not the appropriate locus of moral evaluation. What matters is the fairness of final decisions, such as diagnoses, resulting from collaboration between clinicians and algorithms. We argue that affirmative algorithms can permissibly be deployed provided the resultant final decisions are fair. (shrink)

This paper analyses the question what other healthcare professions should learn from nursing ethics, e.g. what should medical ethics learn from nursing ethics. I first analyse and reject all strong versions of the claim that nursing ethics is unique, because nursing is a unique practice. I then move to the question of whether the link between nursing ethics and nursing theory can be a model for other areas of healthcare ethics. I provide an analysis of the possibility of (...) creating a theory of medicine and find that there cannot be a theory of medicine, and I argue briefly that this finding is also applicable to nursing. If there cannot be a theory of nursing, this entails that nursing ethics cannot be justifiably based on such a theory. In the final section, I then analyse the success of nursing ethics in resisting certain of the vices of Anglo‐American analytic ethics, in particular the reductionism and individualism that characterizes much of healthcare ethics. I conclude that other healthcare ethics could usefully learn from this aspect of nursing ethics. (shrink)

BackgroundEthical problems in everyday healthcare work emerge for many reasons and constitute threats to ethical values. If these threats are not managed appropriately, there is a risk that the patient may be inflicted with moral harm or injury, while healthcare professionals are at risk of feeling moral distress. Therefore, it is essential to support the learning and development of ethical competencies among healthcare professionals and students. The aim of this study was to explore the available literature (...) regarding ethics education that promotes ethical competence learning for healthcare professionals and students undergoing training in healthcare professions.MethodsIn this integrative systematic review, literature was searched within the PubMed, CINAHL, and PsycInfo databases using the search terms ‘health personnel’, ‘students’, ‘ethics’, ‘moral’, ‘simulation’, and ‘teaching’. In total, 40 articles were selected for review. These articles included professionals from various healthcare professions and students who trained in these professions as subjects. The articles described participation in various forms of ethics education. Data were extracted and synthesised using thematic analysis.ResultsThe review identified the need for support to make ethical competence learning possible, which in the long run was considered to promote the ability to manage ethical problems. Ethical competence learning was found to be helpful to healthcare professionals and students in drawing attention to ethical problems that they were not previously aware of. Dealing with ethical problems is primarily about reasoning about what is right and in the patient’s best interests, along with making decisions about what needs to be done in a specific situation.ConclusionsThe review identified different designs and course content for ethics education to support ethical competence learning. The findings could be used to develop healthcare professionals’ and students’ readiness and capabilities to recognise as well as to respond appropriately to ethically problematic work situations. (shrink)

The U.S. and Canadian health care systems are more similar than is commonly believed. This article debunks some of the powerful myths about these health care systems and opens up the discussion for greater policy learning from both sides of the border. Cross-border comparisons can yield a number of lessons from common policy challenges such as cost control, physician organization and payment, and the organization of health coverage and services for Native Americans and Indigenous Canadians.

This essay argues that service-learning pedagogy is an important tool in pre-health humanities education that provides benefits to the community and produces more compassionate, culturally competent, and community-responsive future healthcare professionals. Further, beginning this approach at the baccalaureate level instills democratic and collaborative values at an earlier, crucial time in the career socialization process. The discussion focuses on learning outcomes and reciprocity between the university and community in a Medical Humanities course for junior and senior premedical students, (...) an elective in the premedical curriculum. The course includes an experiential learning element in which students shadow physicians and a service-learning component in which students complete medically-relevant service work, working with partners such as the veteran’s hospital, a hospice home, and organizations that serve individuals with disabilities. We cover topics such as narrative medicine, ethics, cross-cultural medicine, patient/practitioner relationships, the human life cycle, and the illness experience, and the writing, discussion, and reflection we engage in is enriched by the real-world experiences from which the students are able to draw. The shadowing and service experiences and the classroom texts and topics combine to form a symbiosis that leads to especially meaningful teaching and learning outcomes. (shrink)

The application of artificial intelligence and machine learning technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surro...

The study reported in this paper focused on nursing students' learning and, in particular, their integration of caring science in theory and practice. An educational model incorporating educational drama was developed for implementation in three different teaching contexts within the nursing and midwifery study programmes at a Swedish college. A central aim was to understand the dynamics of educational drama in the healthcare context and its impact on learning and teaching. Using a phenomenological approach, seventeen students and (...) six teachers were interviewed and their experience of drama as an educational method explored.The research findings illustrate the meaning of learning and teaching that is sensitive to students' lifeworld experiences. In order to be a successful method for closing the gap between caring science theory and practice, not only the educational drama, but teaching in general, must be anchored in the lived world of the students - that is, their experiences of health and care. While embodied reflection, as a key factor in integrating theory and practice, was shown to be well supported by educational drama, it was also found that "the method" tends too readily to take over and govern teaching and learning. The findings of this study further indicate how learning in practice and embodied reflection can be supported by the inclusion of well-chosen caring science theory to cast light on caring practice dilemmas. (shrink)

Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I analyze the shortcomings of an ML system currently deployed in the USA to (...) predict patients’ likelihood of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems’ decision-making processes can be captured through the lenses of Miranda Fricker’s account of hermeneutical injustice. I further argue that ML-induced hermeneutical injustice is particularly harmful due to what I define as an automated hermeneutical appropriation from the side of the ML system. The latter occurs if the ML system establishes meanings and shared hermeneutical resources without allowing for human oversight, impairing understanding and communication practices among stakeholders involved in medical decision-making. Furthermore and very much crucially, an automated hermeneutical appropriation can be recognized if physicians are strongly limited in their possibilities to safeguard patients from ML-induced hermeneutical injustice. Overall, my paper should expand the analysis of ethical issues raised by ML systems that are to be considered epistemic in nature, thus contributing to bridging the gap between these two dimensions in the ongoing debate. (shrink)

Char et al. aspire to provide “a systematic approach to identifying … ethical concerns” around machine learning healthcare applications, which includes artificial intelligence and...

The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not (...) yet been sufficiently explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society. (shrink)

Machine learning is an increasingly significant part of modern healthcare, transforming the way clinical decisions are made and health resources are managed. These developme...

In their article “A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning,” McCradden et al. highlight the various gaps that emerge when artificial intelligen...

The study reported in this paper focused on nursing students’ learning and, in particular, their integration of caring science in theory and practice. An educational model incorporating educational drama was developed for implementation in three different teaching contexts within the nursing and midwifery study programmes at a Swedish college. A central aim was to understand the dynamics of educational drama in the healthcare context and its impact on learning and teaching. Using a phenomenological approach, seventeen students and (...) six teachers were interviewed and their experience of drama as an educational method explored.The research findings illustrate the meaning of learning and teaching that is sensitive to students’ lifeworld experiences. In order to be a successful method for closing the gap between caring science theory and practice, not only the educational drama, but teaching in general, must be anchored in the lived world of the students – that is, their experiences of health and care. While embodied reflection, as a key factor in integrating theory and practice, was shown to be well supported by educational drama, it was also found that “the method” tends too readily to take over and govern teaching and learning. The findings of this study further indicate how learning in practice and embodied reflection can be supported by the inclusion of well-chosen caring science theory to cast light on caring practice dilemmas. (shrink)

Increasing adoption of electronic health records in hospitals provides new opportunities for patient data to support public health advances. Such learning healthcare models have generated ethical debate in high-income countries, including on the role of patient and public consent and engagement. Increasing use of electronic health records in low-middle income countries offers important potential to fast-track healthcare improvements in these settings, where a disproportionate burden of global morbidity occurs. Core ethical issues have been raised around the role (...) and form of information sharing processes for learning healthcare systems, including individual consent and individual and public general notification processes, but little research has focused on this perspective in low-middle income countries. We conducted a qualitative study on the role of information sharing and governance processes for inpatient data re-use, using in-depth interviews with 34 health stakeholders at two public hospitals on the Kenyan coast, including health managers, providers and researchers. Data were collected between March and July 2016 and analysed using a framework approach, with Nvivo 10 software to support data management. Most forms of clinical data re-use were seen as an important public health good. Individual consent and general notification processes were often argued as important, but contingent on interrelated influences of the type of data, use and secondary user. Underlying concerns were linked to issues of patient privacy and autonomy; perceived risks to trust in health systems; and fairness in how data would be used, particularly for non-public sector re-users. Support for engagement often turned on the anticipated outcomes of information-sharing processes, as building or undermining trust in healthcare systems. As reported in high income countries, learning healthcare systems in low-middle counties may generate a core ethical tension between supporting a public good and respecting patient autonomy and privacy, with the maintenance of public trust acting as a core requirement. While more evidence is needed on patient and public perspectives on learning healthcare activities, greater collaboration between public health and research governance systems is likely to support the development of efficient and locally responsive learning healthcare activities in LMICs. (shrink)

The research ethics framework put forward by McCradden et al. to support systematic inquiry in the development of artificial intelligence and machine learning technologies in healt...

The ‘best interests’ standard is a highly seductive standard in English law. Not only does it appear to be fairly uncontroversial but it also presents as the most sensible, objective and ‘fair’ method of dealing with decision making on behalf of those who are perceived to be the most vulnerable within society. This article aims to provide a critical appraisal of how the standard has been applied within family law, to outline how the standard is to be applied within (...) class='Hi'>healthcare law and, finally, to assess the relevance of the family law experience of the best interests standard to the operation of the standards as envisaged by the MCA. (shrink)

Background:This article combines foundational and empirical aspects of healthcare education and develops a framework for teaching ethical theories inspired by pragmatist learning theory and recent work on the concept of moral resilience. It describes an exemplary implementation and presents data from student evaluation.Objectives:After a pilot implementation in a regular ethics module, the feasibility and acceptance of the novel framework by students were evaluated.Research design:In addition to the regular online module evaluation, specific questions referring to the teaching of ethical (...) theories were added using simple (yes/no) and Likert rating answer formats.Participants and research context:At the Bern University of Applied Sciences, a total of 93 students from 2 parallel sub-cohorts of the bachelor’s program in nursing science were sent the online survey link after having been exposed to the same modular contents. A total of 62% of all students participated in the survey.Ethical considerations:The survey was voluntary and anonymous. Students were free to write their name and additional comments.Findings:Students consider ethical theories—as taught within the proposed framework—as practically applicable, useful, and transferable into practice.Discussion:Teaching ethical theories within the proposed framework overcomes the shortcomings described by current research. Students do not consider the mutually exclusive character of ethical theories as an insurmountable problem.Conclusion:The proposed framework is likely to promote the effectiveness of healthcare ethics education. Inspired by pragmatist learning theory, it enables students to consider ethical theories as educative playgrounds that help them to “frame” and “name” the ethical issues they encounter in daily practice, which is seen as an expression of moral resilience. Since it does not advocate a single ethical theory, but is open to the diversity of traditions that shape ethical thinking, it promotes a culturally sensitive, ethically reflected healthcare practice. (shrink)

What happens when a patient-interfacing conversational artificial intelligence system (CAI)—AI that combines natural language understanding, processing, and machine-learning models to autonomously...

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might (...) improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

The ethics of artificial intelligence and machine learning exemplify the conceptual struggle between applying familiar pathways of ethical analysis versus generating novel strategies. Mel...

Until recently, moral case deliberation (MCD) sessions have mostly been facilitated by external experts, mainly professional ethicists. We have developed a train the facilitator programme for healthcare professionals aimed at providing them with the competences needed for being an MCD facilitator. In this paper, we present the first results of a study in which we evaluated the programme. We used a mixed methods design. One hundred and twenty trained healthcare professionals and five trainers from 16 training groups working (...) in different healthcare organisations throughout the Netherlands were included. After completion of the programme, participants feel sufficiently confident and equipped to facilitate an MCD session. Feeling competent does not mean that participants have no doubts or questions left. Rather, they are aware of their limitations and see the need for continuous learning. According to the respondents, the actual exercise of facilitating MCD during and in between the training sessions contributed most to the development of competences necessary for being an MCD facilitator. Respondents without prior experience of participating in MCD sessions felt less competent after the training than those who had participated in MCD sessions before. Self-attributed competence varied between participants with different professional backgrounds. (shrink)

Background During the first wave of the new coronavirus (COVID-19) pandemic, the sudden increase in hospitalised patients put medical facilities in southern Switzerland under severe pressure. During this time, bachelor’s degree programs in nursing, physiotherapy and occupational therapy were disrupted, and students in their second year were displaced. Students experienced the continuous reorganisation of their traineeship as healthcare facilities adapted to a climate of uncertainty. Purpose The aim of this study was to investigate the degree of moral distress and (...) the ethical issues most often encountered by physiotherapy, nursing and occupational therapy students enrolled in a traineeship during the first wave of the COVID-19 pandemic in southern Switzerland. Participants and research context The sample consisted of 102 participants, and the response rate was 81.6%. Research design Based on a pragmatic approach, a mixed-method with a convergent design was adopted. Data were collected between 30 April 2020 and 14 May 2020, via a survey administered to all occupational therapy, physiotherapy and nursing students in their fourth semester. Ethical considerations Given that no vulnerable persons were involved, the Ethics Committee of Southern Switzerland waived authorisation. However, all measures were put into place to protect participants by guaranteeing their anonymity and confidentiality. Results and discussion The data analysis showed that the main source for moral distress was ‘poor teamwork’ and that the moral issues encountered most often by students were related to the appropriateness of care and working conditions, with a clear reference to students’ own safety and that of their loved ones; the other concerns reported included the loss of learning opportunities and the perceived lack of technical knowledge and skills. Conclusions This survey offers a faithful overview of physiotherapy, nursing and occupational therapy students’ experience during the first pandemic wave. This study also identifies some key recommendations for healthcare professions’ education. (shrink)

What is healthcare professionalism? -- Teaching and learning healthcare professionalism -- Assessing healthcare professionalism -- Identity-related professionalism dilemmas -- Consent-related professionalism dilemmas -- Patient safety-related professionalism dilemmas -- Patient dignity-related professionalism dilemmas -- Abuse-related professionalism dilemmas -- E-professionalism-related dilemmas -- Professionalism dilemmas across national cultures -- Professionalism dilemmas across professional cultures.