The next stage of the NHS digitalisation journey

Stefan Hogendoorn, chief technology officer at Cloud Technology Solutions, outlines the key benefits that machine learning and AI could provide to revolutionise the NHS

Stefan Hogendoorn

Since its inception in February 2019, NHSX – the Government’s new dedicated unit leading the NHS’s digital transformation journey – has introduced a number of initiatives to improve IT strategies across the health sector.

In summer, it announced it was investing £250m into Artificial Intelligence (AI) technology.

The investment is designed to help with some of healthcare’s toughest challenges, including researching new treatments and supporting the NHS’s time-strapped workforce.

We only need to look to our neighbours to see how other healthcare institutions are utilising technologies like Machine Learning (ML) to make more-informed decisions and improve patient care

And, in October, it launched a centre of excellence platform, where healthcare providers can seek advice on securing the best deals with technology suppliers.

These initiatives have put the NHS on track towards realising better outcomes from its ongoing digital transformation.

And, as it continues its journey, lessons from similar initiatives across Europe could provide key learnings that allow the NHS to generate even-greater benefits.

Learning from our European neighbours

We only need to look to our neighbours to see how other healthcare institutions are utilising technologies like Machine Learning (ML) to make more-informed decisions and improve patient care.

Leiden University Medical Centre in the Netherlands is a prime example of an organisation that has dramatically reduced time spent on administration for clinical practitioners through the help of ML.

Prior to using the technology, the research centre found that its clinicians were spending almost as much time on administrative tasks, like typing up patient notes, as they were on patient care, putting significant pressure on resources.

To overcome this, the centre started recording the patient consultation conversations.

Using ML technology, the dialogue is automatically transcribed and the system recognises medical terms to provide an instant analysis.

Parts of the conversation are then automatically sorted into answers to the anamnesis questions.

Physicians can also submit the notes of the appointment to the system with the click of a button.

For an organisation under as much pressure as the NHS, adopting ML can seem like a daunting and onerous task

Using ML in this way helps clinicians provide a much-quicker diagnosis while also reducing a massive administrative burden.

Meanwhile, the German Cancer Consortium is using ML to help with cancer detection – another one of NHSX’s goals within its £250m investment.

The consortium is using the technology to create a new classification method for head and neck cancer based on chemical DNA changes.

In some cases of head and neck cancer, patients can develop lung cancers. However, it can often be very difficult to tell whether these represent an existing cancer or a separate primary lung cancer.

To overcome this, researchers have used methylation data from hundreds of head and neck cancer to train a ML network to be able to distinguish between lung cancer and head and neck cancer. By doing so, so they have created a ML solution that can distinguish between the two cancers, with a 99% degree of accuracy.

Embracing a new way of working

These examples show the great benefits that nascent technologies can provide and help make a strong case for healthcare providers to take an open and receptive attitude to digital transformation.

But, for an organisation under as much pressure as the NHS, adopting ML can seem like a daunting and onerous task. It is, in fact easier, to adopt than ever before through systems offered by Google and Microsoft.

It is encouraging to see NHSX driving forward initiatives that can help the NHS harness the potential of new technologies

Beyond the platforms themselves, the NHS, like many other major organisations considering ML, should also consider appointing teams of data scientists who understand ML technology to lead the technology’s adoption internally. This will both help the NHS to integrate ML with existing systems and help ensure the right skills are in place to measure the ongoing success of the technology.

It is encouraging to see NHSX driving forward initiatives that can help the NHS harness the potential of new technologies. And, as we look to the future, a culture of openness around the use of innovative new technology like ML and AI will be crucial to further improving patient outcomes and advancing the NHS’s digital journey.

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