February 24th 2022

Hope and Health: using data to rebuild and recover

The health and care sector and its workforce have endured two incredibly difficult years. As 2022 begins and the NHS begins to look to a “post pandemic world”, whatever that may mean, rebuilding and recovering from COVID-19 are obvious focusses. Technology will inevitably play a part, and the opportunity to use digital approaches to do more with the same, or even less, resource will doubtless be something that leaders embrace. Here are some predictions for the year ahead in health and care.

Decoration Element

AI will continue to be held up as the cure all
No buzzword seems to be more prevalent across the technology world than “artificial intelligence”. Putting aside the debates around what people mean when they use the term and whether true AI is possible, the noise arounds it implies AI will bleed into all aspects of our modern lives, certainly including health and social care. I prefer to be specific, so focusing on machine learning, which is very much a reality today, and does offer opportunities to improve care, leaving deep learning to the researchers. But we are still very early in our understanding of machine learning: more focus is needed on what things we can do with it. It also risks introducing, or exacerbating, blind spots in our ethical and safety considerations which need to be given serious thought.

Machine learning will become more important in freeing up staff to do their jobs
While AI aspirations still feel at times completely unrealistic, opportunity to use machine learning to incrementally improve services is real. We should start with the low hanging fruit of processes currently dealt with by care staff which could more easily be handled by a computer. This is exemplified by the adoption of Robotic Process Automation by the NHS, with NHS Shared Business Services saving an estimated 300k hours to date and hospital trusts now beginning to automate outpatient appointment booking. There are countless other elements of care and governance similarly ready for the implementation of machine learning and other data science approaches. It is short-sighted to see this purely as a headcount reduction exercise: by moving tedious or repetitious tasks from staff to machines we allow talented people to spend their time doing jobs which are both interesting and beyond the scope of computers. I’m sure in the next few years we will see an increase in decision support for clinicians, but until we have a greater understanding of the limitations of this technology, and until we get a firm hold of the regulatory elements of utilizing this tech in medical products, I suspect this remains an aspiration rather than a reality.

Data strategy
Whether or not we are “sick of experts”, everyone loves to quote a statistic. The health and care sector generates huge amounts of data, but it is frequently siloed and inaccessible to those who could use it. The pooling of data (and rapidly increased scope of collection) allowed the government and NHS access to up to date evidence and to rapidly model a changing threat as the pandemic progressed. As integrated care systems mature, their remit to provide care for a population, across the boundaries of primary, secondary, community and population health will require a joined-up view of data. The first step in making that happen is writing and implementing a data strategy which clearly defines the purpose, needs and objectives and links those to concrete actions to enable sharing and utilisation of data in a safe, ethical and useful way. No element of the system exists in isolation, even if problems manifest more acutely in specific places; without a view across a system, the challenges health and care faces will not be overcome. We are seeing those requirements come to market, and how they are implemented is something we will watch with interest.

It is a sad fact that prejudice and bias are found everywhere, including within health & care. Technology can so often, as we have seen recently, exacerbate inequalities in the system, but looking at data and outcomes across different social groups we can see the effect of these biases in real terms. This may be a more aspirational prediction, but I would like to see real progress made in two ways this year. First, health and care organisations need to take a deep, unvarnished look at how their systems affect patients differently based on their socio-demography. Secondly, they need to be transparent about how they are performing, the changes they are making, and over time, how things are improving. Led by some very capable professionals who have been advocating in this space for some time now, health and care bodies are beginning to think about these things. With the Office for Health Improvement and Disparities now issuing guidance, policy and research, we are keen to see how organisations implement these changes, and whether they will result in measurable improvements to the disparities we know exist across health and care.

This is the most aspirational of my predictions, perhaps best characterised as hope. NHS England has identified investment in the workforce as one of its priorities for 2022. While increasing staff will definitely go some way towards alleviating the burden, I am curious to see what other themes will emerge from the pressures that may be able to be alleviated in ways other then simply throwing more people at the problem, particularly as in most skills groups those people don’t exist. Elements such as the machine learning and RPA previously discussed will hopefully alleviate some of the pressure and stress, but I am hopeful we will see some innovative use of technology to both identify and reduce the challenges and stress staff have faced over the last 2 years and hopefully enable them to recover and maybe even move on that little bit more easily.

By Neil Mason, Head of Healthcare Strategy

Decoration Element
Decoration Element Decoration Element