用于成人社会护理的新兴技术——以人工智能(AI)技术的家庭传感器为例。

Jon Glasby, Ian Litchfield, Sarah Parkinson, Lucy Hocking, Denise Tanner, Bridget Roe, Jennifer Bousfield
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引用次数: 0

摘要

背景:数字技术是NHS和社会护理的重点,是改善护理和解决压力的一种方式。具有人工智能能力的基于传感器的技术是一种可能有用的技术,尽管存在需要解决的证据差距。目的:本研究评估了一个使用具有人工智能功能的家庭传感器(化名为“IndependencePlus”)的技术示例如何在英格兰的三个案例研究地点实施。这项研究的重点是决策过程和执行。设计:第一阶段包括快速文献回顾、九次访谈和三个项目设计小组。第二阶段包括从三个社会关怀网站(20个访谈)收集定性数据,并与技术提供商和监管机构进行了三次访谈。结果:•预计该技术将改善护理计划,降低社会护理系统的成本,帮助预防和响应需求,支持独立生活,并为那些依靠护理及其护理人员的人提供保证。•传感器无法收集必要的数据来创造预期的效益。该系统的几个技术方面降低了它的灵活性,工作人员使用起来很复杂。•在决定是否采用人工智能方面,似乎没有系统的决策过程。在缺乏这种机制的情况下,一些环境因素影响了采购决策。•将基于人工智能的技术纳入现有的社会护理提供模式,需要改变现有的资助模式和护理途径,以及劳动力培训。•技术支持的护理解决方案需要强大的数字基础设施,而这对于许多需要护理和支持的人来说是缺乏的。•短期服务压力和危机管理意识不利于获得人工智能潜在长期利益所需的文化。局限性:面临着重大的招聘挑战(特别是在依靠护理和护理人员的人员方面),特别是与COVID-19的压力有关。结论:本研究证实了一些常见的实施挑战,并增加了对一项已在社会关怀中实施的技术的具体决策过程的见解。我们还确定了与管理和分析数据以及将侧重于预防的技术引入侧重于处理危机的环境有关的问题。这有助于填补文献空白,并与专员、社会保健提供者、技术提供者和决策者分享实践经验。未来的工作:我们强调了我们的发现对未来实践的影响,并与案例研究网站分享了这些发现。我们还根据我们的发现开发了一个工具包,供其他人在成人社会护理中实施新技术(https://www.birmingham.ac.uk/documents/college-social-sciences/social-policy/brace/ai-and-social-care-booklet-final-digital-accessible.pdf)。由于我们的研究结果反映了之前关于常见实施挑战的文献,以及一些技术“承诺过多、交付不足”的趋势,因此需要做更多的工作,将研究结果纳入政策和实践。研究注册:伯明翰大学研究伦理委员会(ERN_13-1085AP41, ERN_21-0541和ERN_21-0541A)的伦理批准。资助:本项目由国家卫生与保健研究所(NIHR)卫生服务和交付研究项目(HSDR 16/138/31 -伯明翰、兰德和剑桥评估中心)资助。
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New and emerging technology for adult social care - the example of home sensors with artificial intelligence (AI) technology.

Background: Digital technology is a focus within the NHS and social care as a way to improve care and address pressures. Sensor-based technology with artificial intelligence capabilities is one type of technology that may be useful, although there are gaps in evidence that need to be addressed.

Objective: This study evaluates how one example of a technology using home-based sensors with artificial intelligence capabilities (pseudonymised as 'IndependencePlus') was implemented in three case study sites across England. The focus of this study was on decision-making processes and implementation.

Design: Stage 1 consisted of a rapid literature review, nine interviews and three project design groups. Stage 2 involved qualitative data collection from three social care sites (20 interviews), and three interviews with technology providers and regulators.

Results: • It was expected that the technology would improve care planning and reduce costs for the social care system, aid in prevention and responding to needs, support independent living and provide reassurance for those who draw on care and their carers. • The sensors were not able to collect the necessary data to create anticipated benefits. Several technological aspects of the system reduced its flexibility and were complex for staff to use. • There appeared to be no systematic decision-making process in deciding whether to adopt artificial intelligence. In its absence, a number of contextual factors influenced procurement decisions. • Incorporating artificial intelligence-based technology into existing models of social care provision requires alterations to existing funding models and care pathways, as well as workforce training. • Technology-enabled care solutions require robust digital infrastructure, which is lacking for many of those who draw on care and support. • Short-term service pressures and a sense of crisis management are not conducive to the culture that is needed to reap the potential longer-term benefits of artificial intelligence.

Limitations: Significant recruitment challenges (especially regarding people who draw on care and carers) were faced, particularly in relation to pressures from COVID-19.

Conclusions: This study confirmed a number of common implementation challenges, and adds insight around the specific decision-making processes for a technology that has been implemented in social care. We have also identified issues related to managing and analysing data, and introducing a technology focused on prevention into an environment which is focused on dealing with crises. This has helped to fill gaps in the literature and share practical lessons with commissioners, social care providers, technology providers and policy-makers.

Future work: We have highlighted the implications of our findings for future practice and shared these with case study sites. We have also developed a toolkit for others implementing new technology into adult social care based on our findings (https://www.birmingham.ac.uk/documents/college-social-sciences/social-policy/brace/ai-and-social-care-booklet-final-digital-accessible.pdf). As our findings mirror the previous literature on common implementation challenges and a tendency of some technology to 'over-promise and under-deliver', more work is needed to embed findings in policy and practice.

Study registration: Ethical approval from the University of Birmingham Research Ethics Committee (ERN_13-1085AP41, ERN_21-0541 and ERN_21-0541A).

Funding: This project was funded by the National Institute of Health and Care Research (NIHR) Health Services and Delivery Research programme (HSDR 16/138/31 - Birmingham, RAND and Cambridge Evaluation Centre).

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