The recent growth in online technology has led to a rapid increase in the sharing of health related information globally. Health and social care professionals are now using a wide range of virtual communities of practice (VCoPs) for learning, support, continuing professional education, knowledge management and information sharing. In this article, we report the findings from a review of the literature that explored the use of VCoPs by health and social care professionals to determine their potential for interprofessional education and collaboration. We employed integrated review methods to search and identify relevant VCoP articles. We undertook searches of PubMed and Google Scholar from 2000, which after screening, resulted in the inclusion of 19 articles. A thematic analysis generated the following key issues related to the use of VCoPs: 'definitions and approaches', 'technological infrastructure', 'reported benefits', 'participation issues', 'trust and privacy and 'technical ability'. Based on the findings from this review, there is some evidence that VCoPs can offer an informal method of professional and interprofessional development for clinicians, and can decrease social and professional isolation. However, for VCoPs to be successful, issues of privacy, trust, encouragement and technology need to be addressed.

ObjectivesThis article presents the findings from a scoping review which explored the nature of interprofessional online learning in primary healthcare. The review was informed by the following questions: What is the nature of evidence on online postgraduate education for primary healthcare interprofessional teams? What learning approaches and study methods are used in this context? What is the range of reported outcomes for primary healthcare learners, their organisations and the care they deliver to patients/clients?SettingThe review explored the global literature on interprofessional online learning in primary healthcare settings.ResultsThe review found that the 23 included studies employed a range of different e-learning methods with contrasting course durations, use of theory, participant mix, approaches to accreditation and assessment of learning. Most of the included studies reported outcomes associated with learner reactions and positive changes in participant attitudes/perceptions and improvement in knowledge/skills as a result of engagement in an e-learning course. In contrast, fewer studies reported changes in participant behaviours, changes in organisational practice and improvements to patients/clients.ConclusionsA number of educational, methodological and outcome implications are be offered. E-learning can enhance an education experience, support development, ease time constraints, overcome geographic limitations and can offer greater flexibility. However, it can also contribute to the isolation of learners and its benefits can be negated by technical problems.

BackgroundAdvances 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.ObjectivesThe 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 methodsA 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.ResultsSearches 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.ConclusionThere 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.ImplicationsIn 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.