Closing the MSK inequality gap: how digital triage can widen access

Musculoskeletal (MSK) conditions affect around 20.8 million people in the UK and account for roughly one in five GP appointments. But the burden isn't shared equally. Chronic pain and reduced mobility fall hardest on people living in the most deprived communities, on women, and on specific ethnic minority groups – a reflection not just of clinical need, but of where people live, the work they do, and the care they can reach.

As digital tools and AI reshape MSK care, a real question sits underneath the excitement: will they close this gap, or widen it? The honest answer is that it depends entirely on how these tools are designed, trained and deployed. Get it right, and digital triage can become one of the most powerful levers we have for fairer care. Get it wrong, and we risk perpetuating the very inequalities we're trying to solve.

A hidden inequality in plain sight

MSK-related health in the UK is not evenly distributed. In England, prevalence rises from 17% in the least deprived areas to 21% in the most deprived. In Scotland, rates more than double – from 12% to 26%. In Wales, the picture is similar: 13% rising to 20%. Chronic pain itself, which affects somewhere between 19 and 29 million people across the UK, is more common in the North of England than the South, and highest of all in the North East.

The numbers look even more stark when broken down by gender and ethnicity. 

36% of women in the UK live with an MSK condition, compared with 28% of men. Black Caribbean adults report prevalence rates of 21.7%, and Pakistani women 29.1% – both well above the 18.4% national average.

The causes are multifaceted. Cultural stigmas can delay when individuals seek help, and diagnostic bias can mean pain is under-recognised in some groups. Standard care pathways often don't account for language, traditional health beliefs or community-specific risk factors. When care is designed around an assumed "average" patient, the people who don't fit that mold are bound to fall through.

A system under strain

All of these factors sit atop a healthcare system that is already at breaking point. The NHS elective care backlog stood at 7.3 million cases in early 2026. Trauma and orthopaedics – the main MSK specialty – has the longest waiting list in England, with more than 800,000 people waiting. 

Access to physiotherapy and specialist MSK services varies enormously by postcode – urban centres tend to have multiple clinics within easy reach, while people in rural areas, coastal towns and deprived urban pockets often face long journeys to the nearest service. Patients without a car, on low incomes, or with limited English face the steepest barriers, and they're often the same patients navigating referral systems that assume confident phone use, digital literacy or the ability to chase up a missed letter. 

The longer the wait, the greater the risk that a treatable condition becomes a chronic one. 

How digital triage can intervene

Done correctly, AI-enabled triage is a practical way to cut through this. It can take detailed symptom information, match patients to the right pathway – self-management, physiotherapy, or specialist referral – and do it in minutes rather than months. Clinicians stay in the loop, but what changes is the speed and consistency in which patients can access the right care.

The evidence is already building. Sandwell and West Birmingham NHS Trust is using AI-enabled digital triage and supported self-management tools, and has already saved 1,240 clinical hours over 12 months – hours that were redirected to face-to-face appointments for more urgent cases, contributing to an 8-week reduction in waiting times. 

In NHS Highland, digital triage is saving around £134,000 in GP time and £29,000 in physiotherapy appointments each year, with 75% of assessed patients managing their condition independently through clinically-supported self-management.

This is what we see in our own work with Phio across the NHS. When digital triage is built around clinical reasoning rather than bolted on top of existing pathways, it consistently frees up clinician time for the patients who need it most. That's the mechanism that matters for closing the inequality gap: not replacing human care, but making sure it reaches the people who have historically been at the back of the queue.

If models are trained on data that reflects diverse populations: by accounting for deprivation, ethnicity, gender and occupation - digital triage can actively identify and fast-track patients from underserved groups. 

Why explainability matters

There’s a real risk with any AI in healthcare: that opaque systems quietly replicate the biases already present in the data. In MSK care, that could mean favouring patients who articulate symptoms well in written English, or under-prioritising pain in groups whose symptoms have been historically minimised. 

Explainable AI is the antidote. When clinicians can see how a decision was reached - the weight given to severity, to deprivation, to demographic context - bias becomes visible and easier to combat. Fairness stops being a hope and starts being a design standard.

That's why we believe any digital MSK tool serving the NHS should be built with clinicians in the loop, on diverse real world training data, with transparent logic, and co-designed with the communities it serves. 

A catalyst, not a cure

No one should pretend that AI will fix MSK inequality on its own. Disparities have long since spanned into housing, employment, healthy life expectancy, and decades of under-investment in community care. But we do have a genuine opportunity here. Smarter triage and explainable data models can streamline access, unlock clinical hours, and surface the patients who've been waiting longest in silence.

At EQL, our mission is to make quality MSK care accessible to anyone, anywhere. Phio is how we do that – and the more we learn from deploying it across the NHS, the clearer it becomes that the technology works best when fairness is a design principle, not an afterthought. That's the standard we hold ourselves to, and the one we'd encourage every digital health provider to meet.

Used correctly, AI will help us build a fairer and more efficient version of MSK care.

About Phio

Phio is EQL's AI-powered digital MSK assessment platform, designed to help patients access the right care at the right time. Used across the NHS and by occupational health providers, Phio combines clinical intelligence with a patient-first experience to reduce waiting times, free up clinician capacity, and widen access to quality MSK care.

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