The NHS and social care workforce have been at the core of the UK’s front-line response to the coronavirus (COVID-19). The dedication and professionalism that has been shown, in the face of the worst pandemic since the Spanish flu a century ago, has been second to none. But well before the pandemic hit – at the time of the 2019 general election – we argued that workforce issues were the biggest challenge for health and social care, with staff shortages a major concern. Nowhere are these shortages more visible than in nursing, that ‘finest of the fine arts’, indispensable to providing quality care.
Nursing has long been the key area of NHS staffing shortages – if anything, the word ‘long’ falls short here, as the issue was documented nearly a century ago. In England alone, the NHS was short of around 38,000 nurses in June 2020. Nursing accounts for one in four full-time equivalent (FTE) jobs in the NHS hospital and community health services, but close to one in two vacancies. Vacancies offer a good measure of shortages as they highlight posts that the NHS is funding but cannot fill.
In June, the vacancy rate in nursing, the FTE ratio of vacancies to jobs, exceeded 10% in the NHS, as opposed to the less than 7% vacancy rate overall. Social care meanwhile has a vacancy rate of over 12% in registered nursing. These high vacancy rates are a concern as they often signal declines in the quality of care provision. The Health Foundation has argued that the government’s target to hire 50,000 additional nurses by the end of this parliament does not go far enough.
To address the nursing shortfall, we need a comprehensive long-term health and social care workforce strategy. That needs two things. First, well-informed projections of how many nurses will be needed in the coming years across different regions and sectors (such as acute care and mental health), based on up-to-date data and research. Second, a sound understanding of how those projections are influenced by changes in policy, with investments in domestic training and immigration policy being two good examples.
Why not stick to tried and tested modelling?
Existing attempts to project nurse numbers into the future commonly follow a ‘stock and flow’ approach, which means taking the latest available number of nurses and applying estimates of annual leaver and joiner rates to project that for future years. Imagine that there are 100 nurses in total, of which 10 leave in a year with another 12 joining in their place – it’s easy to see that leaves us with 102 nurses.
But this method only takes us so far – it is the ‘art of the possible rather than the state of the art’. It implicitly assumes that joiner and leaver rates will not change. It also doesn’t account for changes in policies affecting young people’s decisions to train to be nurses – for instance, the removal of a nursing bursary in England in 2016 likely contributed to a reduced nurse student intake in the following 3 years.
The traditional method also fails to consider the wider labour market. Nurses, like most of us, think of their pay levels not just in numerical terms, but relative to alternative jobs they could apply for. Experience matters too – a younger nurse might find a 12-hour daily shift manageable, but older nurses might wish to prioritise work-life balance, childcare and the prospects for career progression alongside intrinsic motivation and calling.
To overcome these issues, the REAL Centre and DAS are using a ‘system dynamics’ approach to model nurse supply. In simple terms, that is a mathematical approach that enables a model to account for individual decisions and behaviour (for example, whether to train as a nurse or, having graduated, whether to work for the NHS or the private sector), which conventional methods fail to do. This can also help us explore the relative importance of the different drivers of nurse labour supply (such as pay rates, career prospects and job opportunities in the wider economy).
Even a very good model has limits – the assumptions and approaches used can matter hugely. But unlike a traditional approach, system dynamics can consider variables that affect each other in ‘feedback loops’ (for instance, the number of nurses trained this year could affect university placements 3 years from now, which in turn influences future trainee numbers). It can therefore provide richer insights into the impacts of future policy changes (around COVID-19 or the post-Brexit immigration system, for instance). The REAL Centre model will help us to explore how the number of nurses working in different fields and regions in England (say in mental health in the Midlands, or social care in London) might change over the next two decades in response to changes in policy.
We expect the model to be ready for wider use by the end of 2021. DAS has been hard at work and this paper outlines their progress so far.
In the future, we plan to use the model to obtain updated projections of nurse supply based on the latest data. These projections will account for policy changes, data updates and emerging outputs from ongoing and upcoming work around health care demand and social care modelling. While we recognise that a model is only part of the answer to tackling the longstanding shortages in nursing, we hope our efforts will lead to better informed policies for our highly valued nurses. We will continue to engage with key stakeholders and interested parties. Watch this space.
Nihar Shembavnekar is an Economist at the Health Foundation.
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