Toward Responsible Artificial Intelligence in Long-Term Care: A Scoping Review on Practical Approaches

Lukkien, Dirk; Nap, Henk Herman; Buimer, Hendrik; Peine, Alexander; Boon, Wouter; Ket, Hans; Minkman, Mirella; Moors, Ellen H.M.

doi:10.1093/geront/gnab180

Cited by 34 publications

(20 citation statements)

References 77 publications

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“…Technologies to support older adults and their caregivers at home is an area of strong growth for design. Policy makers, providers, and family caregivers are looking to technological solutions and investing in artificial intelligence (AI) and other technologies that monitor activity and safety of older adults, with a particular focus on Alzheimer’s disease and related dementias (e.g., the AAL Joint Platform of the European Union and AGE-WELL Initiative in Canada) [ 25 , 69 , 91 , 127 , 129 , 138 ]. In the U.S., health care payment systems including Medicaid are now beginning to cover a range of technologies that have remote monitoring functions [ 17 , 28 ], foretelling significant growth as lack of third party reimbursement had impeded startups in the aging space [ 19 , 129 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

Berridge

Zhou

Lazar

et al. 2022

Designing Interactive Systems Conference

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Studies fnd that older adults want control over how technologies are used in their care, but how it can be operationalized through design remains to be clarifed. We present fndings from a large survey (n=825) of a well-characterized U.S. online cohort that provides actionable evidence of the importance of designing for control over monitoring technologies. This uniquely large, age-diverse sample allows us to compare needs across age and other characteristics with insights about future users and current older adults (n=496 >64), including those concerned about their own memory loss (n=201). All fve control options, which are not currently enabled, were very or extremely important to most people across age. Findings indicate that comfort with a range of care technologies is contingent on having privacy-and other control-enabling options. We discuss opportunities for design to meet these user needs that demand course correction through attentive, creative work.

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Section: Background and Related Workmentioning

confidence: 99%

Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

Berridge

Zhou

Lazar

et al. 2022

Designing Interactive Systems Conference

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“…The burden of decision making about care without knowing that person’s preferences is also a source of stress for care partners ( Horowitz et al, 2004 ; Whitlatch et al, 2009 ). For these reasons, researchers have identified the need to educate families and people living with dementia about the technologies’ risks and benefits in a way that is accessible, clear, and meaningful to them ( Berridge et al, 2021 ; Lukkien et al, 2021 ; Meiland et al, 2017 ; Robillard et al, 2018 ; Thorstensen, 2018 ).…”

Section: Background and Objectivesmentioning

confidence: 99%

Preliminary Efficacy of Let’s Talk Tech: Technology Use Planning for Dementia Care Dyads

Berridge

Turner

Liu

et al. 2023

Innovation in Aging

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Background and Objective Care partners of people living with dementia require support to knowledgeably navigate decision making about how and when to use monitoring technologies for care purposes. We conducted a pilot study of a novel self-administered intervention, “Let’s Talk Tech,” for people living with mild dementia and their care partners. This paper presents preliminary efficacy findings of this intervention designed to educate and facilitate dyadic communication about a range of technologies used in dementia care and to document the preferences of the person living with dementia. It is the first of its kind decision-making and planning tool with a specific focus on technology use. Research Design and Methods We used a one group pretest, post-test design and paired t-tests to assess change over two time periods in measures of technology comprehension, care partner knowledge of the participant living with mild Alzheimer’s disease’s (AD) preferences, care partner preparedness to make decisions about technology use, and mutual understanding. Thematic analysis was conducted on post-intervention interview transcripts to elucidate mechanisms and experiences with Let’s Talk Tech. Results Twenty-nine mild AD dementia care dyads who live together completed the study. There was statistically significant improvement with medium and large effect sizes on outcome measures of care partners’ understanding of each technology, care partners’ perceptions of the person living with dementia’s understanding of each technology, knowledge of the person living with dementia’s preferences, decision-making preparedness, and care partners’ feelings of mutual understanding. Participants reported that it helped them have important and meaningful conversations about using technology. Discussion and Implications Let’s Talk Tech demonstrated promising preliminary efficacy on targeted measures that can lead to informed, shared decision making about technologies used in dementia care. Future studies should assess efficacy with larger samples and more diverse sample populations in terms of race, ethnicity, and dementia type.

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“…However, current guidelines are generally highly abstract and leave much room for interpretation as to how these principles can be practically applied and contextualized to specific technologies like AI-DSSs and in specific contexts such as LTC [27,28]. While scholars recognize the importance of a more context-specific conceptualization of these principles, multiple literature reviews have shown that only few studies specify practical approaches to responsible AI innovation for specific application domains, and this is especially true for applications of AI in LTC [6,8,29,30].…”

Section: Introductionmentioning

confidence: 99%

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

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

Preprint

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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 AI-assisted 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 identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address different factors important to the responsible embedding of AI-DSSs in practice.

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Toward Responsible Artificial Intelligence in Long-Term Care: A Scoping Review on Practical Approaches

Cited by 34 publications

References 77 publications

Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

Preliminary Efficacy of Let’s Talk Tech: Technology Use Planning for Dementia Care Dyads

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

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