Organising through compassion: The introduction of meta-virtue management in the NHS

An auto-ethnographic reflection on the nature of nursing in the UK during the Covid-19 pandemic

In this article I discuss the effects on the patient experience of isolation nursing during the CoronaVirus Disease (COVID)-19 pandemic. An unintended consequence of isolation nursing has been to distance patients from nurses and emphasise the technical side of nursing while at the same time reducing the relational or affective potential of nursing. Such distanced forms of nursing normalise the distal patient in hospital. I consider ways in which this new form of distanced nursing has unwittingly contributed to the continued commodification of nursing care in the British NHS. Autoethnography is used to describe and reflect on the illness experience, the experiences of caregivers and the sociocultural organisation of health care. The findings discuss three areas of the illness experience: intimate nursing care; communication; the 'distanced' patient experience.

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Background

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.

Objectives

The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?

Materials and methods

A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.

Results

Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.

Conclusion

There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.

Implications

In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

"You Forget to Apply It to Staff": A Compassion-Focused Group for Mental Health Inpatient Staff. An Exploration of the Barriers to Attendance

This study explored the feasibility and acceptability of an experiential compassion-focused group intervention for mental health inpatient staff. Findings demonstrated that although participants found sessions enjoyable, and reported a number of benefits, the group attrition was high. Semi-structured interviews were conducted to explore issues related to group dropout. Thematic analysis highlighted overarching systemic challenges to attendance, and five key themes emerged: The Nature of the Ward; Slowing Down Is Not Allowed; It is Not in Our Nature; Guilt & Threat; We Are Not Important. Clinical implications, limitations and practice recommendations to support group attendance are also addressed.

Professional identity reconstruction: Attempts to match people with new role expectations and environmental demands

Organizations increasingly find themselves in circumstances that generate a need for creating novel identities to deal with novel situations. Through a qualitative study of a professional education programme for military career officers, I explore the reconstruction of professional identities in light of what is perceived as a complex, demanding and changing environment. I found that the programme promoted images and worldviews of an ideal and desired professional identity, which did not match the role transitions and expectations to be enacted by the participants. In addition, the findings show how cultural and organizational dynamics constrained processes of identity reconstruction in the learning context. Implications of the study (e.g. how to theorize and learn from attempts to match people with new role expectations and environmental demands) are discussed. By building bridges across socialization theory, identity work and research on identities in context, and hereby integrating micro- and macro perspectives on professional identity reconstruction, existing theory is elaborated. The article concludes by pointing to the analytical value of exploring how professionals in later stages of their careers struggle to adopt timely and relevant identities and how we better understand the challenges stemming from this identity reconstruction work.

Managing capability in specialist nursing practice

Raising concerns over another nurse's clinical practice and capability presents significant challenges, particularly in specialist clinical nursing roles. However, the support provided to nurses who raise such concerns is not always optimal. While compassionate leadership in healthcare has been stated as a priority to develop safer workplaces for staff, it must be balanced against the need to manage staff undergoing capability management processes. This article discusses how enhanced training and support for both line managers and staff can improve the capability management processes.