Predicting the Need For Care
Earlier this month, the Social Care Institute for Excellence (SCIE) published ‘Beyond Covid: New thinking on the future of adult social care’.
One of its key recommendations is that, instead of a focus on firefighting in response to acute crises, we need a radical shift in emphasis towards prevention to develop a robust social care system.
One of the difficulties with anticipating the need for care is that, for many people and their families, the issue arises unexpectedly…often after the loss of a relative, or an acute hospital admission that tips the balance of scales towards a situation where a person is now unable to cope at home by themselves. Furthermore, whilst (in general) we know that the elderly and those with specific co-morbidities are more likely to need care, each person’s circumstances and their ability to cope when these circumstances change is completely different.
To complicate matters still further, the options for meeting individuals’ care needs ranges from family support, external domiciliary care which may be visiting or live-in, private care vs voluntary sector, through to residential and nursing home care. There is therefore a complex matrix of demand, which is itself unpredictable, and potential sources for supply.
In other areas of medicine, we recognise that an effective system of prevention requires prediction. For example, when someone’s blood pressure begins to creep above a certain threshold, a doctor may recommend they take a medication; if a person experiences specific symptoms such as chest pain, they may be advised to have a procedure to stent the blood vessels- preventing a serious, acute situation from arising.
So what are the ‘symptoms’ that may indicate a more urgent need for an individual to plan their social care? This is a question to which no-one knows the answer. Recent hospital admissions? Number of medical conditions? Self-evaluation of an individual or their family about how they would cope? The views of their GP or nurse?
The data we can now gather from routine tracking systems such as Hospital Episode Statistics (HES) and the Clinical Practice Research Datalink (CPRD), coupled with an ability to rapidly and securely obtain opinions from individuals in the era of the smartphone, gives hope for finding a nuanced mechanism for predicting care. The problem is not straightforward, but finding an answer could be truly invaluable.
Please note that the views expressed here are those of the author alone and not necessarily those of any other person or organisation