Dementia Carers' Perspectives on Paper- and Artificial Intelligence (AI)-Based Assessment Tools: A Qualitative Think-Aloud Study Using the COM-B Framework.

IF 2.8 4区 医学 Q2 CLINICAL NEUROLOGY
Labhpreet Kaur, Alys Wyn Griffiths, Louis Stokes, Judith Harrison, Xingyi Song, Daniel Blackburn
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Abstract

ObjectivesCarer observations provide valuable longitudinal and contextual information for dementia assessment, but traditional informant-based questionnaires may limit reporting of nuanced, fluctuating changes. Artificial intelligence (AI)-based tools may offer greater flexibility to report, but their successful uptake depends on carer capability and acceptability. Despite this, little is understood about carers' views on existing informant questionnaires and emerging AI-based tools, therefore, this study explored carers' perspectives on barriers and facilitators influencing engagement with paper- and AI-based assessment tools.MethodsTen carers of people living with dementia completed think-aloud interviews, while reviewing the Cambridge Behavioural Inventory-Revised (CBI-R) and two AI-based tools, CognoSpeak™ and LUMEN (Language Usage for Monitoring Early Neurodegeneration). Using a hybrid inductive-deductive approach, themes related to barriers and facilitators to engagement were identified and subsequently mapped onto the Capability, Opportunity and Motivation-Behaviour (COM-B) framework.ResultsCarers valued the CBI-R's simplicity and breadth but found its structure, complex language and limited free-text space restrictive. Verbal, narrative responses felt more natural and less burdensome, therefore, AI-based tools were seen as more engaging and better able to collect richer narratives, with the added advantage of remote access, however, digital literacy, sensory impairments, privacy concerns could limit uptake. Participants stressed that AI should support, not replace, human-led care.ConclusionEngagement depended on individual capabilities, available opportunities and motivation; factors fundamental to successful uptake. Future tools should be co-designed to ensure accessibility, cultural sensitivity and clinical relevance.

痴呆症护理人员对基于纸张和人工智能(AI)的评估工具的看法:使用COM-B框架的定性有声思考研究。
护理人员的观察为痴呆症评估提供了有价值的纵向和背景信息,但传统的基于信息的问卷调查可能会限制对细微变化的报告。基于人工智能(AI)的工具可能为报告提供更大的灵活性,但它们的成功采用取决于护理人员的能力和可接受性。尽管如此,我们对护理人员对现有的信息调查者问卷和新兴的基于人工智能的工具的看法知之甚少,因此,本研究探讨了护理人员对影响使用纸质和基于人工智能的评估工具的障碍和促进因素的看法。方法10名痴呆症患者的护理人员完成了有声思考访谈,同时回顾了剑桥行为量表修订版(CBI-R)和两个基于人工智能的工具,CognoSpeak™和LUMEN(监测早期神经变性的语言使用)。使用混合归纳-演绎方法,确定了与参与障碍和促进因素相关的主题,并随后将其映射到能力,机会和动机-行为(COM-B)框架中。结果调查对象对CBI-R的简便性和广度评价较高,但认为其结构、复杂的语言和有限的自由文本空间是限制性的。口头、叙事反应感觉更自然,负担更少,因此,基于人工智能的工具被认为更有吸引力,能够更好地收集更丰富的叙事,具有远程访问的额外优势,然而,数字素养、感官障碍、隐私问题可能会限制使用。与会者强调,人工智能应该支持而不是取代人类主导的护理。结论敬业度取决于个人能力、可获得的机会和动机;成功吸收的基本因素。未来的工具应共同设计,以确保可及性,文化敏感性和临床相关性。
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: Journal of Geriatric Psychiatry and Neurology (JGP) brings together original research, clinical reviews, and timely case reports on neuropsychiatric care of aging patients, including age-related biologic, neurologic, and psychiatric illnesses; psychosocial problems; forensic issues; and family care. The journal offers the latest peer-reviewed information on cognitive, mood, anxiety, addictive, and sleep disorders in older patients, as well as tested diagnostic tools and therapies.
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