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 National Health Service (NHS) and social care as a way to improve care and address pressures. Sensor-based technology with artificial intelligence (AI) 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 AI 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 no systematic decision–making process in deciding whether to adopt AI. In its absence, a number of contextual factors influenced procurement decisions.
• Incorporating AI-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 is not conducive to the culture that is needed to reap the potential longer-term benefits of AI.

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 around a technology. We have also identified issues such as dealing with 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, providers, technology providers and policy makers.

Future work: We highlight the implications for future practice and will seek to share these with case study sites. We will also work to develop these initial questions into a toolkit for use as a resource for others implementing new technology. 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 Research (NIHR) Health Services and Delivery Research programme (HSDR 16/138/31 – Birmingham, RAND and Cambridge Evaluation Centre).