Artificial Intelligence in Long-Term Care: Technological Promise, Aging Anxieties, and Sociotechnical Ageism

Neves, Bárbara Barbosa; Petersen, Alan; Vered, Mor; Carter, Adrian; Omori, Maho

doi:10.1177/07334648231157370

Cited by 15 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI-DSSs are not regarded as potential decision-makers in the nursing process but rather as instruments, and by some even as anthropomorphized agents, such as personal coaches or mentors, that could proactively aid caregivers in becoming aware of certain care needs and adaptively responding to these needs. While these perspectives do not necessarily cover the entire spectrum of opportunities of AI-assisted decision-making, they correspond with previous studies on expectations, opportunities and applications of AI in LTC (eg, [2,7,8,46]).…”

Section: Main Findingssupporting

confidence: 79%

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

JMIR Nursing

View full text Add to dashboard Cite

Background: While use of artificial intelligence (AI)-based technologies such as decision-support systems (AI-DSSs) could help sustaining and improving the quality and efficiency of care, their deployment also creates ethical and social challenges. In recent years, there has been a growing prevalence of high-level guidelines and frameworks to provide guidance on responsible AI innovation. However, few studies specify how AI-based technologies such as AI-DSSs can be responsibly embedded in specific contexts such as the nursing process in the long-term care (LTC) for older adults.Objective: Opportunities and prerequisites for responsible AI-assisted decision-making in the nursing process were explored from the perspectives of nurses and other professional stakeholders in LTC.Methods: Semi-structured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists and care centralists. Two imaginary scenarios about the future use of AI-DSSs were developed beforehand and used to enable participants to articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. After first openly discussing opportunities and possible risks associated with both scenarios, six high-level principles for responsible AI were used as probing themes to evoke further consideration on risks of using AI-DSSs in LTC. Further, participants were asked to brainstorm about possible strategies and actions in the design, implementation and use of AI-DSSs to address or mitigate the mentioned risks. A thematic analysis was carried out to identify opportunities and prerequisites for responsible innovation in this area.Results: Professionals' stance towards the use of AI-DSSs is not a matter of purely positive or negative expectations, but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of opportunities and prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to early identification of care needs, guidance in devising care strategies, shared decision-making, and caregivers' workload and work experience. To optimally balance opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AIassisted decision-making in the nursing process were identified: (1) regular deliberation on data collection, (2) a balanced proactive nature of AI-DSSs, (3) incremental advancements aligned with trust and experience, (4) customization for all user groups including clients and caregivers, (5) measures to counteract bias and narrow perspectives, (6) human-centric learning loops, and (7) routinization of using AI-DSSs.Conclusions: Opportunities of AI-assisted decision-making in the nursing process could turn into drawbacks, depending on the specific shaping of the design and the deployment of AI-DSSs. Therefore, we recommend viewing the responsible use of AI-DSSs as a balancing act. Moreover, given the interrelatedness of the ide...

show abstract

Section: Main Findingssupporting

confidence: 79%

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

JMIR Nursing

View full text Add to dashboard Cite

show abstract

“…The ageism felt before and during the pandemic – concerning portrayals of older people as disposable, dependent and technologically incompetent (Cook et al, 2023; Neves et al, 2023b) – strained loneliness management, including uptake of digital technology for those not knowing how to use it. Broader understandings of ‘growing old’ further suffused responses to loneliness, even among those from collectivist cultural backgrounds that typically value ageing (Alejandria-Gonzalez et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

Loneliness in Later Life as Existential Inequality

Neves,

Sanders,

Warren

et al. 2023

Sociology

Self Cite

View full text Add to dashboard Cite

This article engages Göran Therborn’s conceptualisation of existential inequality to explore lived experiences of loneliness in later life. Existential inequality refers to unequal social distribution of personhood, from dignity to autonomy. We argue sociological approaches, like inequality frameworks, are critical to grasp the social nature of loneliness – often missing in related literature. Investigating how people perceive and respond to their loneliness provides a comprehensive understanding of the links between personal/agentic and social/structural dimensions. We apply the idea of existential inequality to two case studies with older people (aged 65+) reporting prolonged loneliness: one encompassing ethnographic data and interviews with care home residents, and a second focusing on diaries produced by older people living alone. Employing existential inequality to frame how older people define, experience and manage loneliness highlights an assemblage of stigmas and marginalisation.

show abstract

“…This has sought to move away from a bio-medical gaze on the older body (e.g., Öberg, 1996 ) and instead focuses on how aging is embodied as an everyday experience. The embodiments and engagements with AI in the everyday lives of older adults are often overlooked and older adults’ agency is rarely discussed in the context of AI ( Neves et al, 2023 ). However, these engagements are crucial to enhance the design and implementation of technologies into the lives of older adults ( van Leersum et al, 2023 ).…”

Section: Black-boxing Ai In Gerontologymentioning

confidence: 99%

Addressing the Black Box of AI—A Model and Research Agenda on the Co-constitution of Aging and Artificial Intelligence

Gallistl,

Banday,

Berridge

et al. 2024

The Gerontologist

View full text Add to dashboard Cite

Algorithmic technologies and (large) data infrastructures, often referred to as Artificial Intelligence (AI), have received increasing attention from gerontological research in the last decade. While there is much literature that dissects and explores the development, application, and evaluation of AI relevant for gerontology, this article makes a novel contribution by critically engaging with the theorizing in this growing field of research. We observe that gerontology’s engagement with AI is shaped by an interventionist logic that situates AI as a black box for gerontological research. We demonstrate how this black box logic has neglected many aspects of AI as a research topic for gerontology and discuss three classical concepts in gerontology to show how they can be used to open various black boxes of aging and AI in the areas: a) the datafication of aging, b) the political economy of AI and aging, and c) everyday engagements and embodiments of AI in later life. In the final chapter, we propose a model of the co-constitution of aging and AI that makes theoretical propositions to study the relational terrain between aging and AI and hence aims to open the black box of AI in gerontology beyond an interventionist logic.

show abstract

Artificial Intelligence in Long-Term Care: Technological Promise, Aging Anxieties, and Sociotechnical Ageism

Cited by 15 publications

References 36 publications

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

Loneliness in Later Life as Existential Inequality

Addressing the Black Box of AI—A Model and Research Agenda on the Co-constitution of Aging and Artificial Intelligence

Contact Info

Product

Resources

About