Closing the accessibility gap to mental health treatment with AI

We explore the impact of AI-enabled self-referral and triage assistant in accessing mental health treatment in the UK. The findings highlight that the tool improves access to mental health services, particularly among minority groups, demonstrating its effectiveness in a real-world setting.
Closing the accessibility gap to mental health treatment with AI

Share this post

Choose a social network to share with, or copy the shortened URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

In today’s world, it is likely that someone you know has faced mental health problems -  it has been estimated that nearly half of us will experience mental health challenges at some point in our lives. This widespread issue is why the World Health Organization (WHO) identifies mental health conditions as a leading cause of disability and disease burden worldwide.

Despite the prevalence of mental health issues, many people hesitate to seek help, often delaying it for years, which can lead to deteriorating health and diminished quality of life. This can be due to perceived barriers, such as a lack of perceived need for treatment, negative attitudes and stigma around mental health, but also structural barriers, like inconvenient service hours that clash with work schedules. These barriers can be even more pronounced among minority and disadvantaged groups such as individuals with ethnic or sexuality minority backgrounds, who often face additional layers of stigma.

Moved by this pressing issue, our team at Limbic uses AI to help make the highest quality therapy to be accessible to everyone, everywhere by alleviating these barriers with innovative digital tools. To turn this vision into reality, we needed to not only co-produce and refine our innovation with mental health services but also gather evidence on the effectiveness of this solution in the real world.

Introducing our solution: a friendly AI-enabled self-referral and triage assistant designed to seamlessly guide patients to the National Health Service (NHS) Talking Therapies services in the UK. It’s a chatbot that pops up when a user visits a service website, and helps them refer into the service. For patients, it means easy and friction-free self-referral, right when they’re seeking help. For clinicians working in mental health services, it means getting all the information they need, including clinical questionnaire answers, right at the point of referral.

The NHS is uniquely situated for a real-world evaluation due to its standardised referral processes, high rate of self-referrals, and the availability of a comprehensive NHS Digital public dataset. The publicly available dataset has data from all NHS Talking Therapies services and has rigorous quality control measures in place, ensuring that we can use high-quality data for the study. This enabled us to conduct a rigorous, real-world observational study, comparing the referral numbers and demographic compositions of services using our chatbot with those that did not use the chatbot. Our goal was to see if our digital front door could truly make a difference in the real world, especially for underserved communities.

The findings showed that the implementation of the self-referral tool led to a nine percentage point increase in referrals to NHS Talking Therapy services (15% increase in services using the chatbot vs 6% increase in control services). Interestingly, increased referrals were particularly pronounced for minority groups, such as non-binary individuals (179% increase) and ethnic minorities (29% increase). Specifically, the referrals among Asian/Asian British and Black/Black British groups rose by 39% and 40%, respectively. Qualitative feedback identified that the main drivers for improved diversity were the tool's human-free nature and its ability to improve patients' awareness of treatment needs. 

Our commitment to increase access to mental health care aligns with the Sustainable Development Goals and NHS Long Term plans in the UK. Through our efforts, we aspire not only to bridge the gap in accessibility to mental health support but also to inspire a broader movement of rigorous real-world research on digital health tools to confirm their real-world effectiveness.

Learn more about Limbic, and the research we do, at

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Mental Health
Humanities and Social Sciences > Behavioral Sciences and Psychology > Clinical Psychology > Mental Health
Health Care
Life Sciences > Health Sciences > Health Care
Artificial Intelligence
Mathematics and Computing > Computer Science > Artificial Intelligence
  • Nature Medicine Nature Medicine

    This journal encompasses original research ranging from new concepts in human biology and disease pathogenesis to new therapeutic modalities and drug development, to all phases of clinical work, as well as innovative technologies aimed at improving human health.