RESUMO
BACKGROUND: Long-term care (LTC) homes face the challenges of increasing care needs of residents and a shortage of health care providers. Literature suggests that artificial intelligence (AI)-enabled robots may solve such challenges and support person-centered care. There is a dearth of literature exploring the perspectives of health care providers, which are crucial to implementing AI-enabled robots. OBJECTIVE: This scoping review aims to explore this scant body of literature to answer two questions: (1) what barriers do health care providers perceive in adopting AI-enabled robots in LTC homes? (2) What strategies can be taken to overcome these barriers to the adoption of AI-enabled robots in LTC homes? METHODS: We are a team consisting of 3 researchers, 2 health care providers, 2 research trainees, and 1 older adult partner with diverse disciplines in nursing, social work, engineering, and medicine. Referring to the Joanna Briggs Institute methodology, our team searched databases (CINAHL, MEDLINE, PsycINFO, Web of Science, ProQuest, and Google Scholar) for peer-reviewed and gray literature, screened the literature, and extracted the data. We analyzed the data as a team. We compared our findings with the Person-Centered Practice Framework and Consolidated Framework for Implementation Research to further our understanding of the findings. RESULTS: This review includes 33 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: (1) accommodate the various needs of residents and health care providers, (2) increase the understanding of the benefits of using robots, (3) review and overcome the safety issues, and (4) boost interest in the use of robots and provide training. CONCLUSIONS: Previous literature suggested using AI-enabled robots to resolve the challenges of increasing care needs and staff shortages in LTC. Yet, our findings show that health care providers might not use robots because of different considerations. The implication is that the voices of health care providers need to be included in using robots. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-doi:10.1136/bmjopen-2023-075278.
Assuntos
Inteligência Artificial , Pessoal de Saúde , Assistência de Longa Duração , Robótica , Inteligência Artificial/tendências , Humanos , Assistência de Longa Duração/métodos , Casas de Saúde , Atitude do Pessoal de SaúdeRESUMO
INTRODUCTION: Previous social prescribing work highlights a range in the types and number of outcomes used in published studies. We aimed to describe social prescribing outcome core areas and instruments to build capacity for future research and program evaluation. METHODS: This was a modified umbrella review following standard guidelines. We registered the study and searched multiple databases (all languages and years); inclusion criteria were peer-reviewed publications containing outcomes for self-described social prescribing for adults aged 18 years and older. The last search date was 9 July 2023. From the included systematic reviews, we identified primary studies using the same inclusion criteria. For primary studies, we sorted extracted outcomes and instruments into six core areas using a published taxonomy. We located information on instruments' description and measurement properties and conducted two rating rounds for (1) the quality of systematic reviews and (2) reporting of instruments in primary studies. We conducted a narrative synthesis of reviews, primary studies and outcomes (PROSPERO 2023 CRD42023434061). RESULTS: We identified 10 systematic reviews and 33 primary studies for inclusion in our review. Outcomes covered most core taxonomy areas, with an emphasis on psychosocial factors (e.g. well-being) and less emphasis on cognition, physical activity, and caregivers and volunteers. We noted few studies provided detailed information on demographic data of participants or measurement properties of instruments. CONCLUSION: This synthesis provides an overview and identifies knowledge gaps for outcomes and instruments used in social prescribing interventions. This work forms the basis of our next step of identifying social prescribing-related outcomes that matter most across interested parties, such as individuals providers and decision makers.