AI diagnostics could help provide more equal access to healthcare

By Dr Rishi Chopra, General Practitioner, Paddington Green Health Centre, Clinical Director, Regent Health Primary Care Network and Chair, Healthcare Central London GP Federation



The NHS is intended to be a free and readily accessible service for all UK residents.

However, potential barriers such as one’s socioeconomic status, postcode, language proficiency, ethnicity, and immigration status, may prevent individuals from receiving equal access to healthcare – often with alarming regularity and consistency.

These social determinants for health, combined with an under-resourced NHS, are worsening health inequality across England.

To reverse this trend, more accessible wider community diagnostic testing has to become a national health priority.

General practices (GPs) are typically the first entry point to healthcare, but accessing appointments can be particularly challenging for those from deprived or marginalised communities, such as people experiencing extreme poverty or homelessness, asylum seekers, immigrants, and traveller communities.

For example, people without the finances to afford technology, such as a phone or the internet, find it harder to make GP appointments.

Language barriers can also pose difficulties for non-native English speakers, as they may struggle to understand how the healthcare system works.

It’s a huge problem given that people from deprived communities are statistically more likely to suffer health problems stemming from issues such as poor diets or living in low-quality housing.

This puts more pressure on GPs and hospitals within these areas, leading to longer waiting times and reducing the amount of care they can provide for this vulnerable group of people.

Of course, those more advantaged people tend to live in more affluent areas where the pressure on local health services is comparatively less acute.

They also have the means to seek care privately, accessing quicker diagnoses and treatment and further widening the gap in health between deprived and less deprived communities.

To put it into perspective, in 2018 to 2020, life expectancy in the most deprived areas of England was 73.5 years for men and 78.3 for women, compared with 83.2 years for men and 86.3 years for women in the least deprived areas.

A pressurised NHS threatens the health and wellbeing of our society

Persistent understaffing means our public healthcare system is already unable to keep up with the demand for its services.

Morale amongst overworked and underpaid NHS workers is low, leading to a high number of people quitting their jobs.

The entire system is clearly creaking and in some parts ready to snap. It is likely there will become a point where the UK won’t be able to offer certain services on the NHS any longer.

Once this happens, the gap in access to healthcare will become gaping, and there is a risk we will see only the more advantaged groups and the private sector will grow.

Inequitable access to healthcare not only leads to worse health outcomes for patients, it has a profound impact on society and the economy.

When patients with chronic conditions are unable to access proper healthcare, their condition worsens and they are often rendered not fit for work, meaning they leave the workforce and are less able to contribute to society.

Instead, poorly managed chronic disease may result in patients bouncing in and out of hospital exacerbating this further.

Addressing health inequalities through AI diagnostics

It is this damaging pattern that we must address first, by improving community access to diagnostics in deprived communities so that long term conditions are identified early before they worsen and so the risk of complications is reduced.

Unfortunately, our current diagnostics approach is fairly limited and heavily focused on managing risk.

GPs use a patient’s presenting complaint along with their past medical history, clinical examinations and their own judgment to determine what they believe to be the diagnosis.

When they believe there is even a slight possibility of something more sinister, patients are typically referred for further testing to rule out or indeed diagnose something more serious.

This process is resource intensive and time-consuming – akin to searching for a proverbial needle in a haystack – and is wholly at odds with the idea of getting out into the community and diagnosing more people.

Even the screening tests that have been rolled out into local UK communities with a reasonable degree of success, such as PCR tests, cervical screens and blood tests, only focus on ruling out specific illnesses.

So it’s time for a change of approach – specifically, to embrace technologies capable of delivering more advanced diagnostics and relieving some of the pressure on our struggling healthcare system.

The recent emergence of AI-powered diagnostics has the potential to address health inequalities and revolutionise healthcare by providing easily accessible, faster and more accurate disease diagnosis.

For instance, I recently joined the advisory board as a volunteer consultant for an Oxford University based spin-off, Pictura Bio, who’s scientists have built a platform that utilises machine learning and pathogen recognition software – a bit like a Face ID for viruses – to analyse medical images and identify infectious diseases in a matter of minutes.

Technologies that offer an immediate and accurate diagnosis allow patients to be triaged for treatment much quicker, ultimately leading to better health outcomes.

And such is the pace of technological advancement, it’s quite conceivable that these highly accurate catch-all tests could be made readily available in community settings, such as GP surgeries, pharmacies, community centers, libraries, and A&E, offering easy access to diagnoses for everyone, regardless of their location or background.

By giving definitive and accurate diagnoses, AI-led tests would help virtually eliminate risk, leaving doctors confident in their decisions and diagnoses, saving valuable time and resource.

Further, by giving people the power to consider self-testing, GP practices wouldn’t be overrun with people seeking a diagnosis for an illness they fear they might have, freeing up more appointments for others.

Bringing our healthcare system back together

AI-driven diagnostic testing can be a game changer for patients, particularly those from deprived areas and marginalised communities, relieving the pressure on the NHS while helping to close the health gap between advantaged and disadvantaged people.

But moving to these type of tests won’t happen overnight, as there are still some challenges to overcome.

For one, the technology must be affordable enough for these communities and conveniently accessible in order to stop people shopping around between other healthcare services.

Achieving community buy-in is particularly difficult because AI is still relatively new and people are wary, especially in marginalised communities where trust of institutions may be lower.

But if we can address these issues and start rolling out AI diagnostics, we can get the NHS back on its feet and ultimately driving drive up patient outcomes.

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