Virtual environments to study emotional responses to clinical communication: A scoping review

Teaching Airway Management Using Virtual Reality: A Scoping Review

Airway management, a defined procedural and cognitive skillset embracing routine tracheal intubation and emergency airway rescue, is most often acquired through an apprenticeship model of opportunistic learning during anesthesia or acute care residency training. This training engages a host of modalities to teach and embed skill sets but is generally time- and location-constrained. Virtual reality (VR)-based simulation training offers the potential for reproducible and asynchronous skill acquisition and maintenance, an advantage that may be important with restricted trainee work hours and low frequency but high-risk events. In the absence of a formal curriculum from training bodies-or expert guidance from medical professional societies-local initiatives have filled the VR training void in an unstructured fashion. We undertook a scoping review to explore current VR-based airway management training programs to assess their approach, outcomes, and technologies to discover programming gaps. English-language publications addressing any aspect of VR simulation training for airway management were identified across PubMed, Embase, and Scopus. Relevant articles were used to craft a scoping review conforming to the Scale for quality Assessment of Narrative Review Articles (SANRA) best-practice guidance. Fifteen studies described VR simulation programs to teach airway management skills, including flexible fibreoptic bronchoscopic intubation (n = 10), direct laryngoscopy (n = 2), and emergency cricothyroidotomy (n = 1). All studies were single institution initiatives and all reported different protocols and end points using bespoke applications of commercial technology or homegrown technologic solutions. VR-based simulation for airway management currently occurs outside of a formal curriculum structure, only for specific skill sets, and without a training pathway for educators. Medical educators with simulation training and medical professional societies with content expertise have the opportunity to develop consensus guidelines that inform training curricula as well as specialty technology use.

Methodological innovations to strengthen evidence-based serious illness communication

BACKGROUND/OBJECTIVE

A growing population of those affected by serious illness, prognostic uncertainty, patient diversity, and healthcare digitalization pose challenges for the future of serious illness communication. Yet, there is paucity of evidence to support serious illness communication behaviors among clinicians. Herein, we propose three methodological innovations to advance the basic science of serious illness communication.

RESULTS

First, advanced computation techniques - e.g. machine-learning techniques and natural language processing - offer the possibility to measure the characteristics and complex patterns of audible serious illness communication in large datasets. Second, immersive technologies - e.g., virtual- and augmented reality - allow for experimentally manipulating and testing the effects of specific communication strategies, and interactional and environmental aspects of serious illness communication. Third, digital-health technologies - e.g., shared notes and videoconferences - can be used to unobtrusively observe and manipulate communication, and compare in-person to digitally-mediated communication elements and effects. Immersive and digital health technologies allow integration of physiological measurement (e.g. synchrony or gaze) that may advance our understanding of patient experience.

CONCLUSION/PRACTICE IMPLICATIONS

New technologies and measurement approaches, while imperfect, will help advance our understanding of the epidemiology and quality of serious illness communication in an evolving healthcare environment.

Evaluation of an intensive workshop on research methods in supportive oncology

OBJECTIVES

Since 2015, the Harvard Workshop on Research Methods in Supportive Oncology has trained early-career investigators in skills to develop rigorous studies in supportive oncology. This study examines workshop evaluations over time in the context of two factors: longitudinal participant feedback and a switch from in-person to virtual format during the COVID pandemic.

METHODS

We examined post-workshop evaluations for participants who attended the workshop from 2015 to 2021. We qualitatively analyzed evaluation free text responses on ways in which the workshop could be improved and "other comments." Potential areas of improvement were categorized and frequencies were compiled longitudinally. Differences in participants' ratings of the workshop and demographics between in-person and virtual formats were investigated with t-tests and Chi-square tests, respectively.

RESULTS

286 participants attended the workshop over 8 years. Participant ratings of the workshop remained consistently high without substantial variation across all years. Three main themes emerged from the "other comments" item: (1) sense of community; (2) passion and empowerment; and (3) value of protected time. Participants appeared to identify fewer areas for improvement over time. There were no significant differences in participant ratings or demographics between the in-person and virtual formats.

SIGNIFINACE OF RESULTS

While the workshop has experienced changes over time, participant evaluations varied little. The core content and structure might have the greatest influence on participants' experiences.

Implementation of game-based training in oral rehabilitation of edentulous patients in an undergraduate dental course

BACKGROUND

Coronavirus disease-19 dramatically changed the learning conditions of dental students, with restricted access to training sessions and clinical practice. The "Playdent" project proposed the integration of serious games (SGs) in the third-year curriculum, based on tailor-made scenarios questioning the first dental visit of edentulous patients, and examined whether training with the games would advance students' learning outcomes.

MATERIALS AND METHODS

Test scores of 89 students, allocated either to a "test" group that accessed SGs during a 4-week test period in addition to conventional lectures or to a "control" group that benefited solely from conventional lectures, were measured before and immediately after the test period. The subsequent satisfaction of students was assessed in the "test" group.

RESULTS

Scores obtained after the 4-week period significantly increased within the "test" group (11.1% ± 24.9%, p = 0.04, degree of freedom [df] = 30) while they did not change within the "control" group (p = 0.21, df = 57). Qualitative feedback expressed by students who played SGs during the 4-week period demonstrated that 71% of them rated the SGs as satisfactory and 91% of them judged the consistency of SGs content with lectures to be satisfactory.

CONCLUSION

Game-based learning showed a positive impact on the learning outcomes of third-year students. Qualitative assessments provide insights into the pertinence of SGs offered in addition to traditional lectures of third-year complete denture courses. SGs may consolidate skills in oral rehabilitation acquired through traditional passive learning formats proposed in preclinical courses.

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.

Toward a basic science of communication in serious illness

High-quality communication can mitigate suffering during serious illness. Innovations in theory and technology present the opportunity to advance serious illness communication research, moving beyond inquiry that links broad communication constructs to health outcomes toward operationalizing and understanding the impact of discrete communication functions on human experience. Given the high stakes of communication during serious illness, we see a critical need to develop a basic science approach to serious illness communication research. Such an approach seeks to link "what actually happens during a conversation" - the lexical and non-lexical communication content elements, as well as contextual factors - with the emotional and cognitive experiences of patients, caregivers, and clinicians. This paper defines and justifies a basic science approach to serious illness communication research and outlines investigative and methodological opportunities in this area. A systematic understanding of the building blocks of serious illness communication can help identify evidence-informed communication strategies that promote positive patient outcomes, shape more targeted communication skills training for clinicians, and lead to more tailored and meaningful serious illness care.