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 person-centred 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.
INTERNATIONAL REGISTERED REPORT
RR2-doi:10.1136/bmjopen-2023-075278