The National and Local Context
There are several factors likely to affect how adult care services will be delivered over the next few years, notably:
- Growth in demand fuelled by demographic pressures such as our ageing society or the rising number of younger people living into adulthood with complex needs
- Resource pressures, both financial and workforce, on care providers, fuelling a drive for service re-design and efficiency
- A shift towards further tailoring and personalisation of services
- Supporting early intervention and prevention to encourage a move away from crisis driven provision
Ageing and demand
England’s population is growing and is forecast to rise from 54.32 million to 61.54 million by 2039 – an increase of 13.3%. Of this projected population, it is expected that those aged 65 or over will make up a greater proportion of society than is currently the case with 14.8 million people (24.1% of the population) being 65 or older by 2039.
Ageing comes with greater challenges to physical and mental health, reducing individuals’ quality of life in later years, and data suggest that this effect may be becoming more pronounced. An overview of adult social care in England published by the Department of Health & Social Care and the Department for Communities and Local Government suggests that “…life expectancy has risen faster than disability-free life expectancy. The proportion and number of older adults who report that their daily activities are limited have increased since 1991”
Not only will we face an increasing demand for adult care services from those in the oldest age groups: the Departments also noted that “better healthcare means that more ill and disabled children reach adulthood and more ill and disabled young adults live longer”, suggesting a growth in demand will from younger adults.
Funding
Recent constraints on Central Government support for local authorities have contributed to an ever greater pressures on adult care. The NAO reported, for example, that 87% of adults are living in local authorities that set their eligibility threshold to meet substantial or critical needs only. This is one cause of the growing number of family carers: Carers UK estimate that 1 in 8 adults (around 6.5 million people) are carers, and by 2037, it’s anticipated that the number of carers will increase to 9 million. Net expenditure (in cash terms) on adult social care has increased by around 6% since 2009/10, although this is largely due to injections of money through short term programmes – core spend by social services departments has remained largely unchanged.
Pressures have led, inter-alia, to a drive for increased efficiency and greater partnership working, sometimes resulting in closer integration of services. The NHS Five Year Forward View, published in 2014, began a concerted drive to integrate health and social care provision, leading to better, more cost effective outcomes for patients/clients. Since then, areas across England have prepared Sustainability & Transformation Plans (STPs) identifying opportunities to meet need more effectively and efficiently across sectors.
Personalisation
Speaking about the NHS Long Term Plan in 2018, the then Health Secretary, Jeremy Hunt, identified whole-person, integrated care with the NHS and social care systems operating as one as a key principle for the future. He also suggested that “the highest possible control [be] given to those receiving support” – setting an agenda for the personalisation of services in meeting future need. Many local authorities are already exploring how a more tailored approach, providing a menu of choices to individual clients, can play a role in future provision. Notwithstanding this, service provision can still be resource driven: the availability or unavailability of a particular service can skew the demand.
Data driven planning and provision
Hampshire & the Isle of Wight’s STP Delivery Plan recognises the importance of “putting in place technology to shift care closer to home and unlock the power of data to improve decision making”. Moreover, there has been further utilisation of technology in the provision of care to service users in an attempt to streamline the process wherever possible.
The potential for data driven planning and the use of AI is great. A simple example might be the use of data to identify geographic clusters of demand to help plan care rounds and allocate carers or agencies. More sophisticated analysis of large volumes of data can help identify trends and patterns that can be used to better plan interventions and gain efficiencies: for example, the correlation between a person’s age, their long-term health conditions and their likely requirement for support. That analysis can dig deeper to examine how factors such as gender, level of deprivation, primary support reason, ethnicity, or previous history of care requirements shape demand.
Using data to shape policy and provision can result in better targeting of provision, whether by geography of user age-cohort. It can also identify patterns; for example, certain needs may be more likely to occur together, or specific localities may have a particularly high demand for a service. Armed with greater knowledge about service users, local authorities can better plan for care provision. That knowledge can also drive innovation: spotting patterns or trends is the first step to re-assessing how best to meet need.
The Care Analytics project draws on the data already held by local authorities and uses it to model future need. We will produce a tool which will allow council officers to use those data in a variety of ways. The first stage of project, when we will be developing the tool and refining the specification, provides an opportunity to discuss with local councils their needs and how best data can be presented to assist in service planning and delivery.
The project will use AI techniques to identify both simple and complex correlations and patterns and use them to predict a likely outcome or identify an optimum intervention. We envisage two primary models as part of the work, firstly a cost model that predicts the cost of a person’s future care, and secondly a transition model, that predicts the likelihood of transition from one category of care (for example domiciliary care) to another (such a residential care home).