Facilitators and barriers to using AI-enabled robots with older adults in long-term care from staff perspective: a scoping review protocol

Hung, Lillian; Wong, Karen Lok Yi; Wong, Joey; Park, Juyoung; Mousavinejad, Abdolhossein; Zhao, Hui

doi:10.1136/bmjopen-2023-075278

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Given the multidimensionality of medical issues, AI has all of the elements needed to address long-standing and entrenched challenges in various medical disciplines. Special emphasis has clearly been given to the treatment of various forms of cancer and oncology [13][14][15], geriatrics [16,17], cardiology [18], and even dentistry [19].…”

Section: Introductionmentioning

confidence: 99%

Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis

Triantafyllopoulos,

Paxinou,

Feretzakis

et al. 2024

Future Internet

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The integration of artificial intelligence (AI) into medical practice has become a critical focus in contemporary medical research. This bibliometric analysis examined the scope of AI utilization across the healthcare spectrum by analyzing a significant body of publications from the Scopus and PubMed databases. After removing duplicates and reviews, a total of 2061 articles were assessed using VOSviewer software (version 1.6.20). The results were organized into two main sections: influential factors and thematic directions of AI integration in healthcare. The first section highlights the most productive countries, authors, and institutions in terms of publications. The second section explores the keywords used in the relevant literature, and identifies the main thematic areas where AI has a significant impact in medical sector. The findings of this study aimed not only to assess AI’s current contributions to medicine in general but also to highlight specific technological advancements across medical departments, offering a comprehensive overview.

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Section: Introductionmentioning

confidence: 99%

Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis

Triantafyllopoulos,

Paxinou,

Feretzakis

et al. 2024

Future Internet

View full text Add to dashboard Cite

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Evaluating Human-Robot Interactions to Support Healthy Aging-in-Place

Sirizi,

Sabet,

Yahyaeian

et al. 2024

J Appl Gerontol

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Declines in older adults’ cognitive and physical health pose challenges to maintaining their independence. Robots can improve independent living and facilitate aging-in-place. Despite recent innovations in healthcare robotics, the use of robots has not advanced significantly among older adults. This review seeks to understand human-robot interactions in older adults, focusing on their experiences and perceptions of robots for independent living. We identified 17 studies that utilized qualitative methods to investigate older adults and/or their caregivers’ experiences and perceptions of robots designed to help older adults improve independent living. Drawing on content analysis, we identified eight themes: usefulness, ease of use, safety, reliability, self-efficacy, satisfaction, emotional connection with the robot and reciprocity, and intention to use. The findings provide insights to improve existing robots and guide future research about designing robots with higher acceptance. This review may have implications for policymakers, practitioners, and researchers working with robotics to support healthy aging-in-place.

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Adoption of Artificial Intelligence–Enabled Robots in Long-Term Care Homes by Health Care Providers: Scoping Review

Wong,

Hung,

Wong

et al. 2024

JMIR Aging

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Background: Long-term care (LTC) homes face the challenges of increasing care needs of residents and a shortage of healthcare providers. Literature suggests that artificial intelligence (AI) enabled robots may solve such challenges and support personcentred care. Scant literature is from the perspectives of healthcare providers, even though their perspectives are crucial to implementing AI-enabled robots. This scoping review aims to explore this scant body of literature to answer two questions: (a) What barriers do healthcare providers perceive in adopting AI-enabled robots in LTC homes? and (b) What practical strategies can be taken to overcome these barriers and facilitate the adoption of AI-enabled robots in LTC homes?Objective: This scoping review aims to explore this scant body of literature to answer two questions: (a) What barriers do healthcare providers perceive in adopting AI-enabled robots in LTC homes? and (b) What practical strategies can be taken to overcome these barriers and facilitate the adoption of AI-enabled robots in LTC homes? Methods: We adopted the Person-Centred Practice Framework (PCPF) and Consolidated Framework for Implementation Research (CFIR) as the primary and supplementary theoretical frameworks to guide our analysis of findings. We are a team consisting of three researchers, two healthcare providers, two research trainees, and a family partner with diverse disciplines in nursing, social work, engineering, and medicine. Referring to the Joanna Briggs Institute (JBI) methodology, our team searched the databases (CINAHL, MEDLINE, PsycINFO, Web of Science, ProQuest, and Google) for peer-reviewed and gray literature.Results: This review includes 35 articles that met the inclusion criteria. We identified three barriers to AI-enabled robot adoption: 1) Perceived technical complexity and limitation, 2) Negative impact, doubted usefulness, and ethical concerns, and 3) Resource limitations. Strategies to mitigate these barriers were also explored: a) Accommodate various needs of residents and healthcare providers, b) Increase understanding of the benefits of using robots and reassure robots can never replace humans, c) Overcome the safety issues, and d) boost interest in the use of robots and provide training. Conclusions:Our findings closely align with three domains of the PCPF: Professionally Competent, Potential for Innovation and Risk-Taking, and Supportive Organizational Systems. The PCPF offers a useful heuristic to guide our analysis of practice innovation. To address limitations in the PCPF, we also integrated elements from the CFIR into PCPF, which provided more constructs when we considered the resource barriers. Our results underscore the necessity of including the voices of healthcare providers and other stakeholders in the research development and implementation phases for AI-enabled robots. Future research should extend this conversation by exploring diverse stakeholder perspectives.

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Facilitators and barriers to using AI-enabled robots with older adults in long-term care from staff perspective: a scoping review protocol

Cited by 5 publications

References 22 publications

Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis

Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis

Evaluating Human-Robot Interactions to Support Healthy Aging-in-Place

Adoption of Artificial Intelligence–Enabled Robots in Long-Term Care Homes by Health Care Providers: Scoping Review

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