Compassionate nursing care and the use of digital health technologies: A scoping review

Ali, Shamsa; Kleib, Manal; Paul, Pauline; Petrovskaya, Olga; Kennedy, Megan

doi:10.1016/j.ijnurstu.2021.104161

Cited by 28 publications

(25 citation statements)

References 43 publications

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“…The analysis demonstrated a significant effect of SE on EE. These findings are consistent with previous research on the acceptance of healthcare systems ( Hsiao et al, 2011 ), home care nursing solutions ( Kohnke et al, 2014 ; Ali et al, 2021 ), telehealth solutions ( Tsai, 2014 ; Liu and Tao, 2021 ), or wearables technologies ( Gao et al, 2015 ; Huaeng et al, 2021 ). Van Houwelingen et al (2018) confirmed the substantial effect of SE on EE for medical videoconferencing.…”

Section: Discussionsupporting

confidence: 91%

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

Baudier¹,

Kondrateva²,

Ammi³

et al. 2023

Technovation

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Section: Discussionsupporting

confidence: 91%

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

Baudier¹,

Kondrateva²,

Ammi³

et al. 2023

Technovation

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“…VR technology and research on implicit bias are perceived to be tools to address bias, prejudice, cultural insensitivity, eroding levels of empathy, and social disparities of health ( Jones-Schenk, 2016 ). In these discussions there is a collective aspiration for AI to reflect human wisdom in the provision of more compassionate ( Lee et al, 2021 ; Ali et al, 2022 ) and “compassionomic” solutions to healthcare (i.e., safe and cost effective) ( Trzeciak et al, 2017 ). Other expressions of optimism relate to the hope of improvements in service efficiency and quality ( Blease et al, 2019 ; Kemp et al, 2020 ); entrepreneurial opportunities ( Shepherd and Majchrzak, 2022 ); and the design of AI technologies that can encourage collective good and increase prosocial behavior ( Day et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…Compassionate behaviors ( Straughair, 2019 ) are taught through pedagogy ( Hendry, 2019 ), learning objectives ( Lown, 2016 ; Sinclair et al, 2021 ; Wang et al, 2022 ) assessment ( Lown et al, 2016 ), and skills sets such as reflective listening ( Braillon and Taiebi, 2020 ; Su et al, 2021 ). Healthcare research has examined compassion from the perspective of: the predictors of compassion in healthcare professionals ( Fernando and Consedine, 2014 ; Bleiker et al, 2020 ; Pavlova et al, 2022 ); how care environment and organizational culture affect compassion ( Casagrande, 2016 ; Ali and Terry, 2017 ; Dev et al, 2019 ; Tehranineshat et al, 2019 ; Wiljer et al, 2019 ; Ali et al, 2022 ); compassion-maintaining strategies and interventions ( Blomberg et al, 2016 ; Terry et al, 2017 ; Flores and Brown, 2018 ; Baguley et al, 2020 ; Hopkins et al, 2021 ; Malenfant et al, 2022 ); compassionate leadership ( Dewar and Cook, 2014 ; de Zulueta, 2015 ; Lown et al, 2019 ; West et al, 2020 ); and regulation of compassionate caregiving ( Harris et al, 2019 ; Pedersen and Roelsgaard Obling, 2019 ). Culturally and critically informed perspectives of compassion highlight that different societies, professional groups, cultures, and generations hold different expectations and views about compassion ( Koopmann-Holm and Tsai, 2017 ; Sundus et al, 2020 ) which change over time ( Salvador Zaragozá et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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“…Another growing trend in nursing competency is related to healthcare information technology. This finding may reflect that information technology in a healthcare system has become a key element due to nurses’ roles becoming increasingly reliant on and inter-twined with digital health tools, such as electronic records, mobile computing devices, telehealth, and robotics [ 27 ]. A systematic review on informatics competency reported that many informatics competency lists had been developed for entry-level nurses in several countries over the past two decades [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…This finding may reflect that information technology in a healthcare system has become a key element due to nurses’ roles becoming increasingly reliant on and inter-twined with digital health tools, such as electronic records, mobile computing devices, telehealth, and robotics [ 27 ]. A systematic review on informatics competency reported that many informatics competency lists had been developed for entry-level nurses in several countries over the past two decades [ 27 ]. Particularly as a result of the COVID-19 pandemic, where there has been an exponential increase in virtual care delivery across the world, further research is needed to evaluate the readiness of new graduates as a healthcare workforce to effectively use digital health tools to support care delivery.…”

Section: Discussionmentioning

confidence: 99%

The Current Contexts of Newly Graduated Nurses’ Competence: A Content Analysis

2022

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Producing and sustaining a competent nursing workforce is imperative to protect the public. Identifying current issues and trends in nursing competence can strengthen insights and provide direction for the future nursing workforce. A summative content analysis was performed; PubMed, CINAHL, and Scopus were searched for content from the last ten years. A total of 3225 titles and abstracts regarding nursing competence were identified and analysed using the big-data analysis software Leximancer. Five themes were discovered from the analysis: (1) standardisation of nursing competencies with emerging competencies, (2) assessment competency levels, (3) graduates’ expectations and achievement, (4) safe and quality practice with teamwork, and (5) competency curriculum development. This found standardised nursing competencies, which suggests prioritising which core competencies should be focused on during education to produce competent generalist professional nurses, and employers could help nursing graduates improve their competence in specialised areas. This review also suggests that further education strategies should be developed to better prepare graduates for culturally safe practice to meet the needs of diverse minority populations and for informatics competency during the COVID-19 pandemic. Competence assessment methods must be extensively investigated to measure nursing competencies both longitudinally and cross-sectionally.

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Compassionate nursing care and the use of digital health technologies: A scoping review

Cited by 28 publications

References 43 publications

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

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

The Current Contexts of Newly Graduated Nurses’ Competence: A Content Analysis

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