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New funding shouldn’t return us to the ‘Old Normal’

Orlando Agrippa, CEO of RwHealth, writes for Health Tech World

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An NHS building against a blue

As well as transforming the way we’ve lived for the past 18 months, COVID-19 has proved to be an absolute watershed event for the NHS. Not only has it reinforced the centrality of the health service to our society, but it’s also shown that, having reached breaking point during the pandemic, the NHS requires serious investment if it is to go on providing the cradle-to-grave healthcare we’ve come to rely on.

On 6 September, the government announced that NHS England is to receive an extra £5.4bn over the next six months to deal with the current situation, with a further £1bn for the rest of the UK. 

While this is great news, it hasn’t been without controversy. This extra cash, and an ongoing package of £12bn a year for both the NHS and social care in Britain, will be funded by an increase in national insurance contributions, something that affects every one of us. 

In the wake of the health service’s heroic response to the COVID-19 crisis, there’s a lot of good will in the country towards the NHS, and it has long been regarded as one of Britain’s proudest post-war institutions. However, with this new funding hitting all of us in the pocket, the public will want to see that the money is being spent wisely and focused as much as possible on clinical care, rather than being absorbed by ongoing admin and managerial costs, as is often the current perception. Accountability and value for money will increasingly become key considerations.

Given that COVID-19 has forced us to review how healthcare is delivered in this country, and with new funding now becoming available, isn’t now also a good time to take a long, hard look at how the NHS can work smarter rather than just perpetuate practices and procedures that are increasingly inefficient and outmoded in today’s world? I’m not talking about running hospital wards as a business and introducing more market forces into healthcare. Rather, I’m more interested in the way that new technologies can be used to improve patient care and diagnosis by making data work harder for healthcare professionals.

For instance, there is a vast amount of information residing in GP and hospital databases that at the moment is only being used discreetly, if at all. What if some of this funding was used to investigate how artificial intelligence (AI)-based technologies can unlock the huge potential value of this data by making it more accessible and easier to analyse and interrogate?

One specific example could be the establishment of ‘real-time’ disease registries within the NHS. Disease registries – essentially a database of patients who suffer from the same condition – are a common feature within modern healthcare. As well as being used in clinical trials, they’re also vital for ongoing research. This might include undertaking epidemiological studies of incidence, prevalence and outcomes, assessing the size of a target population for a clinical trial, profiling the population in terms of comorbidities and demographics or examining the utilisation of healthcare resources such as number of visits, hospitalisations or laboratory tests performed.

The problem is that current disease registries are incredibly cumbersome. They’re time-consuming and expensive to establish, requiring: the coordination of different hospitals and other facilities; a dedicated IT infrastructure for the secure transmission and collation of data; expertise in data cleansing and curation; and ongoing investment to maintain them. 

Yet most, if not all, of the patient-level data of interest in a disease registry is already recorded in various hospital databases and primary care systems. Instead of just using these electronic health records for ad hoc studies, why not integrate them into a unified view which is then updated in real-time? This unified dataset could not only have the capabilities of a registry, but by enabling analysis of data in real-time, it could identify patterns and connections within the disease profile quicker and more dynamically.

With a constant flow of data, and an analytical module sitting atop the unified dataset continuously monitoring the incoming data flow, specific criterion for studies could be detected almost immediately. Similarly, for other requirements, such as monitoring trends in treatment patterns, caseloads and presenting characteristics, analytical modules could provide up-to-the-minute descriptive statistics and immediately identify when pre-defined thresholds have been reached.

More generally, delivering better patient outcomes can often be as simple as ensuring that the information systems involved in their treatment are as easy and intuitive for the healthcare professionals looking after them to use. Today, this is often not the case, with medical teams still struggling to record and retrieve data on vast and ungainly Excel spreadsheets which are no longer fit for purpose, or forced to use complex systems that require intense, specialised training. 

Yet by bringing AI-based data analysis to bear on patient records, initial prognoses and clinical pathways, and making that information available via an easy to use interface, it’s possible for patients to be dealt with in the right way much more quickly, because the healthcare professionals looking after them are no longer being hindered by their own tools. Instead of being bogged down by admin, more clinical time can be freed up, and people can be streamed more efficiently according to the seriousness and immediacy of their condition.

Putting data in front of clinicians in an intuitively usable format like this is very powerful, and can generate huge benefits and better outcomes for patients where speed is nearly always of the essence. For example, cancer treatment is constantly an exercise in saving time, because a faster diagnosis can lead to earlier remedial measures, and ultimately a greater chance of survival and recovery. Every moment that a more intuitive solution saves – no matter how small – has a tangible impact.

There are a number of AI-based technologies currently being trialled within the NHS that are helping to expedite diagnoses and are already leading to significant improvements in patient care. It is vital that some of the new funding for the NHS is used to expand these trials and spread their benefits further throughout the health system.

It’s important to note that the mooted £12bn a year funding is also meant to tackle the social care crisis in this country, alongside bolstering the NHS. While some commentators have pointed out the inadequacy of this figure given the scale of the problem – with the pandemic leading to an unprecedented demand for mental health services – it’s possible to use the allocated money to help social care work more smartly. Enhancing community services through better use of data should play a key role in supporting our nation’s mental health and avoiding demand outstripping capacity in an already overburdened system. 

Service delays within the mental health sector during the pandemic were often caused by a lack of system monitoring facilities and patient management, creating bottlenecks and increasing waiting list times. However, those social care trusts who fared the best already possessed strong data-driven decision making capabilities, and were able to act with agility because of the forecasting and operating models they already had in place. Preventative care that keeps people out of hospital, based on predictive analysis of data, should be a central focus of attention and funding going forward.

Predictive analysis also has a vital role to play in managing the sensitive balance between supply and demand for medical resources, something which became a major pressure point during the worst stages of the pandemic. Again, those hospitals that were already using data-driven solutions and strategies were able to manage patient volume, bed capacity and ventilator availability better than those that weren’t. Predictive healthcare tools can help to mitigate potential impacts on the supply chain and other critical areas, as well as optimise healthcare delivery, in the event of future crises. Again, this is the type of area where a relatively small amount of funding could go a long way.

Ultimately, what we need to do is create an infrastructure within the NHS that supports a variety of data-driven digital applications, which join up the dots between primary, secondary and social care, and enable individual patient journeys to take place. Developing new treatments and new medicines is also vital, and an enormous amount can be done to accelerate this process through more intelligent use of data that already exists, with AI as the agent to unlock its value. And in addition, we can use machine learning and predictive analysis to tackle everything from workforce shortages to wait list profiling.

Let’s not squander this opportunity to look afresh at how the health service and social care system in this country is run, because more than anything, it’s vital that this new funding isn’t just used to return us to the ‘old normal’.

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  1. Pingback: Sunak confirms £2.1 billion to improve NHS digital technology - Health Tech World

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