Blog

How can machine learning and behavioural analytics technology transform social care?

02nd December 2021
How can machine learning and behavioural analytics technology transform social care?

Nick Weston, our CCO, explains how the struggling social care system can be transformed by new machine learning and behavioural analytics technology

At the end of September 2020 the social care sector in England estimated that it faced a massive £6.6 billion in extra costs as a result of the pandemic.

With rising pressures on home and social care, workforce shortages due to Brexit and the global pandemic thrown into the mix, only four percent of social care directors were confident that their budgets would cover statutory duties.

Even with a significant cash injection from the government, including a proposed £1.7 billion per year for social care and hospital discharge, the financial strain on the sector is unrelenting and still fails to plug the funding gap. Although a figure of £150 million has been set aside for digital transformation and technology adoption which signals some positive movement towards the government and the sector being able to embrace more innovative solutions, it fails to recognise the level of investment that is needed and the sheer potential that technology has to transform the sector for the better.

The state of social care

A snap survey of the state of social care services from the Association of Directors of Adult Social Services members that was published on Monday 29th November 2021 has revealed a rapidly deteriorating picture of hundreds of thousands of older and disabled people left waiting for help despite record increases in care being provided to people in their own homes. Almost 400,000 people are now waiting for an assessment of their care needs and more than 1.5 million hours of commissioned home care could not be provided between August and October, an 8 fold increase, up from 250,000 hours between April and June because of a lack of staff, and despite record growth in provision. This paints a stark picture of the unprecedented demand and relentless resourcing pressure placed on social care and a system in no way prepared to meet it.

A changing social care landscape

As life expectancy rises with every generation, the demand for social care from ageing populations who suffer with chronic conditions for longer will only rise too. With the increased pressure on resources and staffing, those in charge of social care delivery will need to find more innovative ways of delivering care, with this even more essential for in-home care and reablement, where budgets are often only second in size to those for residential and nursing provision.

The complications of long Covid are predicted to be an additional challenge, with further work needing to be done to fully understand the longer-term impact that COVID 19 will have on the social care sector.

Using data and machine learning to support social care outcomes

To achieve this system change, new technology needs to be embraced, with the increased use of data and machine learning to improve the quality and outcomes for care whilst also supporting more efficient use of limited resources. Although machine led learning may potentially be seen as an impersonal technology  it is emerging as a major force in the transformation of care through its ability to provide rich insight and prevent manual data manipulation or guesswork, which is welcome relief to overstretched care systems that can then focus on the delivery of care that matters most. In a home care setting this has huge potential to revolutionise the sector.

By using monitoring technology in the home to provide masses of data, spotting patterns and analysing behaviour, this kind of technology makes it possible to establish an individual’s normal daily patterns of behaviour and quickly flag up any anomalies, therefore enabling care to be allocated exactly when it’s needed.

Preventing health decline and enabling better care decisions

Research has shown that elderly people alongside people with learning disabilities tend to have established routines from which they rarely deviate, and so any change from these routines can be an indicator of a change in circumstance or health status.

Analytical solutions can spot when an individual’s behaviour changes or alters from the norm, flagging up warning signs so care providers can intervene early on and take appropriate action. This could initially be a phone call or a visit to the individual to find out why the patient is behaving differently which offers the chance to intervene early on and take preventative measures before health decline can happen.

By creating a ‘flightpath’ of regular behavioural patterns and setting thresholds that are appropriate to the individual and their condition, the accuracy of the data and the insights extracted reduces unnecessary and costly social care callouts and visits, while providing information on which those working in care can base decisions about care and resource allocation.

Patients may also be reluctant or unable to discuss symptoms of a new or emerging problem, however the insights from the data can also provide caregivers, clinicians, and managers with evidence that they could never obtain otherwise.

How monitoring technology can provide insight

Various kinds of sensors are placed in a patient or client’s home, designed to be discreet, and monitor every day behaviours such as the movement or or the use of domestic appliances, removing the need for large unpopular medical hardware or traditional outdated reactive alarm based systems.

Rather than waiting for critical incidents, such as trips and falls, to occur and flag deterioration, machine-led tech can spot the minor changes in behaviour that could indicate health decline or a new symptom. This information is then made available to care professionals to decide whether intervention is required.

A simple example would be less frequent use of taps, kettle or toilet, indicating a potential problem with hydration, even though the client may be maintaining mobility.

Machine-led driven solutions can analyse data from these sensors and provide care providers with information which is meaningful and easy- to understand, allowing service providers and caregivers can give a much better and tailored quality of care.

A new proactive solution for the social care problem

At Lilli we understand that demand for social care is not going to reduce and rising pressures are highlighting an urgent need for a complete change to the system.

We’re confident that by switching from an old fashioned reactive alarm based model whereby health and social care needs are identified and addressed after an issue has arisen, to a new proactive and preventative approach for delivering care through data and technology, the future for the social care sector will look a lot brighter.

By revolutionising the system in this way we can build a system that is not only transformative in its use of technology and the delivery of better health and social care outcomes but is also financially sustainable to help solve the huge resource issues and financial deficits that the sector is facing.

Improving patient outcomes

Machine-led technology is making positive waves in the social care sector with trials with local councils underway resulting in costs being reduced, fewer visits by carers and lower rates of hospital admissions.

Lilli is currently involved in a Pilot with Dorset council that is already showing positive outcomes, saving in the region of £4000 per person annually through reduced care visits.

Advances in technology such as behavioural analytics enable people to remain in their home surroundings for far longer, which is a great gain in human dignity. The technology can empower people to live independently, safely, and happily without feeling they are a burden on the care sector.

The future of the UK care sector is certain to be heavily technology and data driven. Regardless of funding reform, the care sector needs to explore advances in technology that can help provide better results for health and social care organisations, frontline staff delivering care and patients alike.

Do you work in the social care sector? You can find out more about Lilli’s pilot with Dorset council on the BBC News website here.

 

 

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