New and emerging technology for adult social care

Nurse assisting an elderly woman in her home, photo by feliks szewczyk/Adobe Stock

RAND Europe researchers, along with University of Birmingham partners in the NIHR funded BRACE Rapid Evaluation Centre, evaluated home-based sensors with AI capabilities meant to help with adult social care.

What is the issue?

The social care system in England is under pressure, with funding gaps and workforce making it difficult to keep up with rising demands for adult social care. Digital technologies have been put forward as one potential way to help address these pressures. But evidence about their effectiveness is incomplete.

How did we help?

In this study, RAND Europe researchers, along with our University of Birmingham partners in the NIHR funded BRACE Rapid Evaluation Centre, evaluated one example of such technology (home-based sensors with AI capabilities). The evaluation looked at the decision-making process around the technology, what stakeholders expected from the technology, how it was implemented, the experience of care staff, and facilitators and barriers to using the technology. We have drawn on our findings to suggest ways to improve how technologies are selected and implemented in adult social care.

The first stage of the evaluation consisted of scoping work to better understand new and emerging technologies for adult social care. This involved a rapid scan of the literature, key informant interviews (n=9) and three online project design groups. The second stage of the evaluation focused on three case study sites across England where the technology had been implemented. This stage consisted of interviews with senior decision makers, operational leads and frontline care staff at each site (n=20), along with interviews with technology providers and national regulatory bodies (n=3).

What did we find?

Across the case study sites, we found that there was a lack of a shared understanding of what the technology was meant to achieve. Although stakeholders reported wide ranging anticipated benefits (including increasing preventative care; improving assessments and diagnoses; supporting independent living; providing reassurance for those who draw on care and support and their carers; and cost savings to the social care system), it appears that these anticipated benefits were, for the most part, not fully realised. In part, this could be attributable to technical challenges that prevented data from being consistently collected and used, such as issues with internet connectivity, sensor reliability and a lack of training and capacity for front line staff to be able to interpret data meaningfully and at scale.

In many cases, there seemed to be a lack of systematic decision-making processes around the adoption of this technology into adult social care. In particular, senior decision makers were often reported to have selected the technology and then identified a use for it, rather than first considering the issue that it was meant to solve or the population it was meant to help. Furthermore, early decisions were often made without the input of frontline staff or people drawing on care and support, which contributed to mismatches between the functionality of the technology, the population it was meant to be used with and what it was expected to achieve.

Despite these challenges, our findings should not be taken to suggest that this technology, or other similar technologies, have no place within adult social care. Rather, our findings highlight the importance of putting in place robust processes for selecting technology, implementing and evaluating it that can help improve how technology is used in the future.

What can be done?

Drawing on the findings from this evaluation, there are several steps that might help senior decision makers and operational leads improve how technology is selected and implemented in adult social care:

  • Begin by identifying a problem and population, and then seek the best way to address that problem by systematically evaluating potential solutions.
  • Build consensus around what technology is meant to achieve, and include frontline staff and people that draw on care and support in these conversations.
  • Consider what training will be needed to successfully implement technology
  • Create protocols around who will receive data, how it will be interpreted and how it will be used to inform care decisions.
  • Think early about how to evaluate a technology, and what steps will be taken if the technology does not achieve expected outcomes.