Social care has fallen behind… but innovation is happening

In general, we know that social care is behind health when it comes to AI. There are good reasons for this: a more fragmented landscape of providers and commissioners; many organisations still collect data manually; there are fewer technical experts to draw on.

But there are pockets of innovative AI development happening. And that’s why social care leaders asked the AI Lab to put together a collection of case studies, to boost awareness across the sector about what is possible. Each case study starts with the problem to be solved: what are the needs of service users, and how is an AI-powered solution meeting this need?

It’s been a fascinating piece of work – and we'd like to share some of the amazing innovations we found that are beginning to help address some important issues in social care.

CASE 1: Improving night-time checks – AI in residential care

The problem: A residential care provider wants to improve the way it checks on residents at night. Current intermittent in-person checks by carers wake people up and risk missing problems, such as falls or calls for help, at other points during the night. Could there be an alternative?

An AI-based solution: The provider has been working with two start-ups to introduce acoustic monitoring devices in residents’ rooms. These devices automatically send alerts to carers when abnormal patterns of activity are detected and prevent unnecessary disturbances from carers in the night.

CASE 2: Managing and treating pain – AI and dementia

The problem: When people have difficulty vocalising and self-reporting their pain, it can go undetected and untreated. We spoke to a company working on how pain interventions might be better managed for people with dementia.

An AI-based solution: This company’s device – being piloted in UK care homes – takes a 3-second video of a person’s face and applies AI to identify facial micro-expressions indicative of pain. This information is automatically combined with other non-facial indicators of pain – recorded by a carer on a digital checklist – to generate an overall pain score.

CASE 3: A more holistic and preventative approach – AI in domiciliary care

The problem: How can a local authority plan and commission services that offer a more holistic and preventative model of care? We spoke to a local authority working with local partners to combine several health and care datasets related to users of domiciliary care.

An AI-based solution: Using machine learning analytics, they have generated 7 key profiles of users to stratify the needs of these residents. Where there are clusters of residents with a certain profile around specific GP practices, the council will be working with local Primary Care Networks to explore how they could cater for these groups more proactively.

CASE 4: Addressing a lack of care supply - AI to reduce social isolation

The problem: The impact of loneliness on people’s physical and mental wellbeing is significant. And social isolation is likely to be worse in rural areas where, typically, support networks and infrastructure are more thinly distributed.

An AI-based solution: We’ve spoken to a company using its existing geospatial mapping technology to identify “dark patches” within rural areas where demand and supply of social support is mismatched. This technology identifies the types of unmet need and the skills required within communities to address these needs. It also provides a digital marketplace to connect people who need support and care with those who are offering it locally.

These are just 4 out of the 14 case studies we worked on. If you’re interested to find out more, head here to the NHSX website to read them all.

We’d also love to hear from if you have thoughts or questions about the practical application of AI in social care.

Authors: Harris Lorie and Antonio Weiss