Only a quarter of children who need mental health support currently get it from Child and Adolescent Mental Health Services (CAMHS), and those who do often wait until their problems are severe. This project aims to build a new early-identification and prevention service, powered by digital tools and artificial intelligence, that could catch problems before they escalate. The core problem is that CAMHS are stretched and reactive. They intervene late, and many young people—especially those from minority groups or experiencing economic hardship—never access help at all. Meanwhile, data that could predict mental health risk already exists across maternity records, GP notes, schools, and social care, but it is never combined. This project will bring that data together, work out which information matters most, and develop AI algorithms that can flag who needs help and what kind of support would work best for them. If successful, the research could transform a strained public service into a fairer, more efficient system. Instead of a single queue for everyone, young people might be directed to the right service first time—whether that is a digital tool, a community programme, or a specialist clinic. The team will also co-design the service with young people, carers, and clinicians, ensuring the tools are trusted and acceptable. The result could be a template for data-guided, personalised mental health care that intervenes early, reduces long-term harm, and eases pressure on CAMHS.
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(written with PPI panel) Many aspects of a young person's life can affect their mental health(MH), and there is a crisis in our ability to support childhood mental illness. Problems often have to become serious before young people can access Child & Adolescent Mental Health Services(CAMHS). CAMHS are stretched, offering help to only a quarter of those in need, and often intervene late. Early identification and treatment are beneficial, but could swamp services and create even longer waits. Some young people are reluctant to access CAMHS because of stigma (e.g. self-harm). Inequity also limits access (e.g. those experiencing economic hardship or from minority groups). These variations leave many struggling to get help, affecting their health lifelong and their and their families' lives. We need to re-think how CAMHS are delivered. Using digital tools to make CAMHS fairer and more efficient could help young people get the right treatment sooner. For example, apps or websites could be used to: (1) identify problems early before someone needs intensive treatments, (2) signpost young people to the most useful services for them rather than sending everyone to CAMHS, or (3) help predict who would benefit most from which treatments, so young people get the right treatment first time. This could be achieved by harnessing the power of 'big data'. Information (data) about a young person's life could help. For example, the risk of serious problems is indicated by an accumulation of factors such as early childhood experiences (e.g. bullying, neglect, racism), the environment (e.g. housing, diet, the amount of green space near home) or physical factors (e.g. genetics, inflammation, brain chemistry). Data like these are already collected from a range of sources such as maternity, health visitors, GP records, schools and social care, but are never brought together. This information, if brought together, could be used to create digital tools to identify patterns using artificial intelligence (AI). However, there are problems to solve first. We do not know which data are most useful, how best to bring data together securely, or the most effective AI methods. Importantly, we have not got agreement on which information should be used for which purposes. For example, it might be acceptable to use genetic information in a hospital to decide which medication is safest, but maybe not to identify who is at risk of suffering from a problem in the community. We must get this right. In this study, we will access data from a broad range of sources, some of which we will collect and organise in the early stage of this project, and use it to establish the best way to develop digital tools to support CAMHS. We will then work with the public, and experts who work with or have experience of MH problems, to translate AI algorithms into digital tools. These digital tools must be part of a clinical service that can intervene early. We want to create a new early identification and prevention service and establish what digital tools are needed to make early detection work effectively, safely, and fairly. We will bring together experts who are doing ground-breaking work in academia, industry, and the clinic, with policy makers. We want to turn their attention to solving these problems, together with young people, their carers, and people with lived experience. The people whose data is used should direct the building of these tools and new clinical pathways. We need their help thinking about which data should be used for what purposes, for which people, what should happen when a young person is thought to be developing MH problems, and how to use digital tools to support treatment decisions. In later years we will explore the effectiveness of the early identification and prevention approach, create recommendations for overhauling inefficient systems and develop a template for data-guided, individualised, and timely MH interventions for the future.
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