Artificial Intelligence–Based Clinical Decision Support Systems in Geriatrics: An Ethical Analysis

Skuban, T.; Orzechowski, Marcin; Denkinger, Michael; Kocar, Thomas Derya; Leinert, Christoph; Steger, Florian

doi:10.1016/j.jamda.2023.06.008

Cited by 4 publications

(9 citation statements)

References 56 publications

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“…Notwithstanding the positive perspectives regarding the opportunities of using AI-DSSs, the care professionals generally expressed caution about its potential impacts. Despite their limited prior knowledge and expertise regarding the risks of AI, the care professionals have shared a diverse array of interrelated concerns about risks associated with AI-assisted decision making, which mirror findings from previous studies on the ethical implications of using AI-DSSs in healthcare (eg, [21,35,36]). For a large part, these concerns revolved around the heavy reliance of caregivers on AI-DSSs, which might, for instance, cause caregivers to overlook crucial nuances that are beyond the grasp of an AI-DSSs.…”

Section: Main Findingsmentioning

confidence: 56%

“…While various studies have offered conceptual expert analyses and synthesized relevant literature on factors important to the responsible embedding of AI-DSSs in healthcare (eg, [22,34,35]), few have investigated (future) user perspectives on responsible AI-assisted decision making [36]. This study first examined the perspectives of nurses and other professional stakeholders in LTC on the opportunities and risks of AI-assisted decision making in nursing practice, thereby laying the groundwork for the second and main objective: exploring prerequisites for responsible innovation in this area.…”

Section: Introductionmentioning

confidence: 99%

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

Lukkien,

Stolwijk,

Ipakchian Askari

et al. 2024

JMIR Nursing

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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 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...

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Section: Main Findingsmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

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

“…Despite their limited prior knowledge and expertise regarding risks of AI, care professionals discussed a diverse array of risks of AI-assisted decision-making in the nursing process. In accordance with conceptual and empirical analyses from previous studies on the ethical implications of using AI-DSSs in healthcare (eg, [24,[38][39][40][41]), care professionals' comments encompass interrelated concerns about an excessive reliance of caregivers on technology, potentially eroding caregivers' critical decision-making capabilities and causing misguidance towards unsuitable care strategies. These viewpoints tie in with how Nyholm [42] sketches the dual effects of AI on human intelligence; the prospect that AI technologies might serve as a form of cognitive enhancement and the cautionary notion that heavy reliance on AI technologies might also make people less intelligent.…”

Section: Main Findingsmentioning

confidence: 82%

“…In a similar vein, there was a notable skepticism amongst the care professionals participating in this study regarding AI's future capacity to comprehensively anticipate -by itself -people's care needs. After all, it might be difficult or even impossible to fully capture in data and decision rules for AI what contributes to good care and quality of life of an individual person [39,40,55]. Hence, in the context of AI-assisted decision-making, it might become increasingly important to engage in shared-decision-making to get to know clients and optimally respond to their personal needs, goals, interests, preferences and values [20,56].…”

Section: Implications For Research and Practicementioning

confidence: 99%

“…Moreover, shared AI-assisted decisionmaking may be particularly challenging in the care for older people, especially if those people have cognitive impairments. This may hinder older adults' ability to express their feelings and wishes and amplify the risks that nurses and other (in)formal caregivers -consciously or unconsciously -enforce what they think is right [39,40,57,58]. A fruitful direction for future research could be to explore how AI-DSSs could be adequately integrated in shared decision-making processes with older adults and their (in)formal caregivers.…”

Section: Implications For Research and Practicementioning

confidence: 99%

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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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Legal and Ethical Considerations of Artificial Intelligence for Residents in Post-Acute and Long-Term Care

Solaiman

2024

Journal of the American Medical Directors Association

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Artificial Intelligence–Based Clinical Decision Support Systems in Geriatrics: An Ethical Analysis

Cited by 4 publications

References 56 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

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

Legal and Ethical Considerations of Artificial Intelligence for Residents in Post-Acute and Long-Term Care

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