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Library›Open textbooks›New and emerging technology for adult social care – the example of home sensors with artificial intelligence (AI) technology›Findings: Implementation of AI in social care

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New and emerging technology for adult social care – the example of home sensors with artificial intelligence (AI) technology · Findings: Implementation of AI in social care

Findings: Implementation of AI in social care

CC BY 4.0 · National Institute for Health and Care Research · approximately 8 min read

Box 11 provides a summary of Chapter 6 of this report.

Summary of key points

  • There was no apparent protocol at any of the sites that described the process of implementing and evaluating new and emerging technologies.
  • Training was provided for staff, though when provided directly by the technology provider there were reports it failed to appropriately engage care staff.
  • Because of the perceived lack of consultation with care staff during procurement, issues arose with finding a suitable cohort to pilot IndependencePlus.
  • Careful consideration is needed in gathering informed consent from people who draw on care and support, especially where people have cognitive impairments (who, ironically, might be the very group that were intended to benefit the most from the technology).

Sites made no mention of a systematic approach to implementation or of any standard operating procedure (SOP) they would ordinarily use for implementing and evaluating service innovations. As a result, there sometimes appeared to be a lack of structure to the way the implementation was managed. While the technology provider was involved in setting up the sensors, there seemed to be little clarity around the respective roles and responsibilities of the provider and sites, either with regard to the ongoing maintenance of the sensors or in terms of sharing risks/benefits. While some sites had engaged senior staff in designing the implementation of IndependencePlus, the lack of a broader consultation process meant that some front-line staff only became aware of the new technology when the implementation started. In one site, the initial stages of implementation entailed installing the kit in two staff members’ homes to demonstrate how it worked and the nature of the outputs it generated. Though this usefully highlighted some of the key issues around digital connectivity and information governance (see below for further discussion), the extent of these issues also seemed to negatively impact on staff buy-in. The early stages of implementation also revealed significant issues with care practitioners’ digital literacy and varying levels of experience of technology within the workforce (e.g. a survey conducted at site 3 found that 57% of the workforce were uncomfortable using technology). As a result, it was perhaps inevitable that some staff were reluctant to engage with technology for fear their lack of technical expertise would be exposed or were otherwise instinctively suspicious of AI-based technologies:

[Some staff would say]… ‘I don’t really understand technology and this is just going to expose me as somebody [who doesn’t understand technology]…’. Maybe they’re worried about what that says about their intelligence and that’s clearly a fear more than a reality. And then I think there was others were thinking ‘this will never work!’ and then, you know, almost – the detractors sometimes are more difficult because it almost becomes a self-fulfilling prophecy in that I don’t think is going to work and so I’m not going to engage with it and therefore it won’t work and therefore I was right all along.

These attitudes highlighted the importance of consulting and engaging with staff destined to use the technology earlier in the decision-making and implementation process. In this way, their concerns could be surfaced and addressed. As a senior decision-maker in one site reflected:

I think first and foremost with any tech, regardless of what it is, I think the first step forward is always talking to the staff, training the staff and giving them that information before you go ahead and start doing anything. Because I think what’s happened here is we’ve put stuff in and then done a little bit of a ‘Look! We’re doing this!’… and it’s proven to be problematic.

Training

Training focused on the interpretation of the dashboard to enable staff to understand the implications of the data for each individual. The majority of this was delivered directly by IndependencePlus, with some delivered in-house. Perhaps indicative of a broader antipathy towards new technology, but also offering a lesson for any external IT training, there is a need to engage more effectively with care staff. In one site, the training conducted by IndependencePlus seemed to antagonise participants:

The social workers absolutely hated it every single time the IndependencePlus people came down to talk to them because they were too slick, they were too techy, they didn’t understand. And equally on the other side, I’m sure they kept looking at us as if to say ‘What is the matter with these people? Just embrace it!’ So, it was just totally different languages and so I think one of the lessons is you need to have that kind of intermediary, that kind of translator, because it just got their backs up so badly… This one poor guy kept turning up and every single time he’d speak to social workers he’d tell them about this slightly fictionalised case where someone had had a urinary tract infection and how that had prevented them from needing a hospital admission and blah, blah, blah, and of course it drove the social workers nuts because they did not care, quite honestly!

Identifying a suitable cohort

Due in part to the top-down nature of the procurement, issues arose as to exactly where IndependencePlus would sit and who it might benefit. As described in Chapter 4, the technology was expected to benefit a range of users and situations. However, in one site it was discovered very late on that the technology was not appropriate for all the intended cohorts. This might be because the sensors were not specific to individuals (so not suited for shared environments, e.g. in a care home kitchen or supported living facility). In other cases, people with dementia who were confused by the technology sometimes tampered with or removed the sensors, while those in the over-85 age group would frequently turn the hub off at bedtime as they were accustomed to unplugging electrical equipment overnight (this issue is also discussed in Chapter 7):

Well, the original thinking was that we were going to deploy IndependencePlus kits around various cohorts… and it soon became apparent – and again we’ve learned from our mistakes which is always good – that we had actually bought a sizeable amount of tech and were then trying to find people to fit the tech… and very quickly the cohorts and the amounts that we could potentially deploy to, the numbers started reducing and reducing.

Only in one site was there a more concerted effort involving multiple staff groups and the technology provider to identify residents who might be appropriate for the pilot. They used both data impact assessments and diversity impact assessments and engaged with individuals to further inform the acceptability and suitability of the technology in each case.

Seeking informed consent

Processes for seeking informed consent often involved very complex judgements and trade-offs, as those who might benefit the most from IndependencePlus were often those less able to provide fully informed consent. This created a series of moral dilemmas for sites:

I think there’s a lot of complexity to unpick with safeguarding in terms of having people who can’t provide consent for themselves and putting these sensors in their living space and then using that information to determine kinds of care decisions. I think there’s a lot of complexity that no one in the organisation was very comfortable with… is it the ethically right thing to do at the expense, which might be kind of discomfort from that person or not ever really getting to a place where they truly understand what all of it is for – is the value there – does the value for us keeping them safe outweigh them not feeling very safe or not understanding kind of the whole context of the supports that they’re getting?

As part of the consent process, a number of queries were raised by potential users. The use of the phrase ‘artificial intelligence’ caused concerns because of potential misconceptions in the public domain as to its safety (e.g. media stories about the risks of AI-driven cars). As a result, some staff talked about becoming more selective in the language they used, referring instead to the digital technology as ‘enablers’ (as an example):

‘Enablers’ is giving people the information to understand what it’s doing. Labelling anything ‘artificial intelligence’ at this moment in time conjures up all sorts of prejudices, cars driving down the motorway with people asleep at the wheel or reading the newspaper.

Similarly, the technology provider reflected how they had begun working more closely with their customers to support these conversations:

We’re increasing the amount of work we do on how do you have the conversation with a prospective subscriber – so it’s one thing for us to train up social workers in what the data looks like and how to use it, but ultimately they’ve got to go out and have a conversation with someone and say, you know, convince them to have it installed in their home, so how do they have that conversation? So we’re doing a lot of work with them on having that at the moment. And one of the ways that we don’t do that is reflected by one of our dear customers, who I shan’t name, who said, ‘Look, we’ve been going out to people, we’ve been asking if they want an AI installed in their home and they say “no”!’ I’m like, well, you know, would you?!

Staff also were called upon to allay concerns over the aesthetic impact of the sensors in people’s homes, using images of the sensors and hub to reassure potential users. Perhaps most significant were the repeated concerns raised not only by people drawing on care and support but also their families that the presence of monitors in bedrooms and bathrooms risked compromising privacy, which staff acknowledged might be considered invasive:

The challenge with this, even as a concept, is this idea of it’s all a bit Big Brother-like, it’s all a bit you know, sort of a bit ‘spying’… The challenge would be to break down some of the stigma that might come with that. I’m not necessarily saying that it’s true, that it’s like a bit Big Brother-like, but I think that is the perception amongst some people who might be resistant to using the technology, you know. If it’s just there forever, recording how many times I use the toilet, you know, it’s uncomfortable.

← Findings: Decision-making to adopt AI in social careFindings: Early experiences of those using AI in social care →

Attribution. Jon Glasby; Ian Litchfield; Sarah Parkinson; Lucy Hocking; Denise Tanner; Bridget Roe et al. (2023). New and emerging technology for adult social care – the example of home sensors with artificial intelligence (AI) technology. National Institute for Health and Care Research. Copyright © 2023 Glasby et al. Licensed under CC BY 4.0. NCBI Bookshelf NBK593317. CC BY 4.0. Original source ↗. Text reproduced from the Bookshelf XML without changing wording; figures, images and supplements are not reproduced.

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