New and emerging technology for adult social care - the example of home sensors with artificial intelligence (AI) technology.

Abstract

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).