The Government recently released its 10-year Plan for the NHS. After an initial reaction, we have spent the past few weeks reading the plan and discussing it with colleagues in clinical services.  We are encouraged to see a shift to digital named as a key operational driver of change across the NHS. At the CAMHS Digital Lab, we create digital innovations and health informatics to support children and young people’s mental health services. Our work aligns with the plan in many ways – placing prevention, community care, and digital innovation at the heart of clinical services. Read on to find out more about how the CAMHS Digital Lab is already delivering on key ambitions in the 10-year Plan.   

Digital innovations that provide clinicians with more data to support decision-making.  We were pleased to see encouragement for NHS providers to make better use of IT systems, supporting activities like collecting outcome measures from patients as well as using dashboards designed for clinicians to visualise data. With the support of our NHS colleagues, we have successfully developed and implemented these technologies across our local CAMHS teams in Southwark, Lambeth, Lewisham and Croydon. We were interested in the emphasis on the use of ambient voice technology (AVT) to support clinicians by, for example, streamlining note taking. We agree that there is a lot of the potential for these technologies to reduce the administrative burden on clinicians, improve their job satisfaction, and provide more time for patients. There are still questions to be answered to ensure these tools are accurate across different clinical specialties and the needs of different patient groups. We hope to address some of these questions as we prepare for a trial we are conducting of AVT in Croydon and Lambeth CAMHS in partnership with Anathem.

Early intervention and prevention. These were named as critical to supporting children and young people’s mental health, with schools identified as key to implementing this ambition. Our work within the Maudsley Education Consultation Service has supported schools to understand and improve their students’ mental health. Together, we implemented our co-designed myJournE app, developed for students, caregivers, and teachers to monitor mental health risk factors and outcomes, and provide school leaders with a comprehensive, whole school mental health needs assessment. We are also supporting a pioneering pilot of digitally delivered Single Session Interventions for adolescents with anxiety and depression who are on the waiting list for community CAMHS. The goal is to provide short and accessible support to teens who may otherwise be at risk of deteriorating mental health while waiting to access CAMHS. 

In line with the early intervention and prevention aim, the plan highlighted how increasing the collection of information on children’s mental health – i.e., remote monitoring – can support earlier intervention and improve clinical decision-making. This starts in early childhood, with the plan outlining plans to move to the use of a digital ‘e-red book’. In our work within Gen2020, we’re developing a digital platform to track early developmental milestones, aiming to help parents and early childhood services accurately track development and access support more efficiently. We appreciated the Plan’s support for further expansion of data collection across different health services, including patient-reported outcome measures like those we have collected from children and young people through the myHealthE platform. Likewise, wearables – wearable devices like watches that monitor health data such as movement and heartrate – are valuable sources of information. Through our PACES+ project, we have brought wearables into mental health, piloting a device to track activity levels in children and young people trying to find the right dose of  ADHD medication. To support this ambition, we could also monitor mood through apps such as myJournE, currently in use in the international AIMS 2 Trials study capturing the daily experiences of autistic young people. The Plan also highlighted the need for improved ways of assessing the implementation of digital technologies in clinical services, as we are doing through our Digital Therapeutics Assessment Group. 

We’re sharing this work not just to highlight achievements, but to celebrate the parts of the plan that are grounded in strong evidence and are already making a difference in clinical settings. These uses of digital innovation and focus on early detection and prevention also aligns with other NHS priorities like the UK clinical research system transformation. Across our services, we’ve seen how digital innovation can transform the way we support children and young people’s mental health. This innovation is helping families feel more informed and empowered, while giving clinicians more time to focus on what matters most: working closely with families to deliver personalised, effective, and joined-up care. 

For more information please email us at camhsdlab@kcl.ac.uk.

Acknowledgements

Special thanks to Dr. Shuo Zhang and Jessica Penhallow for their support in preparing these blogs.

Spotlight Session: Predicting Outcomes in ADHD 

We were joined by Dr Nicoletta Adamo, who presented her project: “Predicting Outcomes in ADHD: Perspectives and Priorities from Lived Experience.” 

This study is exploring whether a tool can be developed to better predict which children with ADHD are likely to experience fewer difficulties and which may need more support. By looking at a combination of personal traits and environmental factors, the research hopes to understand what drives success—and what gets in the way.

To make the tool as accurate as possible, the study also considers whether genetic information or ‘biomarkers’ could play a role. These can be gathered from a simple salvia sample and look at our genetic makeup, and might tell us about likely future health outcomes for a child with ADHD – for example whether a child is likely to experience depression in adolescence, or likely educational attainment. This study is doing a deep dive into understanding if that information was gathered and shared, how it could best be used to aid, not hinder outcomes. The project will launch in July and involve: 

• Focus groups with young people (16–25), parents, clinicians and teachers
• Exploration of how such tools are perceived—including emotional impact and ethical considerations
• A co-designed, inclusive approach that ensures personalised support care, not stigma

The Community of Practice discussed the study. We discussed the importance of sharing this information about predicted outcomes in a considered way so they do not have unintended consequences that hinder a child’s potential. There was however much discussion also about the potential of biomarkers being able to improve treatment and preventative care by giving more information to clinical services and policy makers to ensure individuals receive the best possible support. 

Get Involved 

Interested in contributing to the conversation or shaping what comes next?
📩 Contact us at pmcyp@slam.nhs.uk 

Let’s keep building this community—together. 

The journey to effective help for challenges to mental health in children can be long. While there are many effective interventions, clinicians are often unsure which intervention will most likely help. So, there is usually a trial-and-error approach, trying one intervention, then another, and sometimes even another, until the most helpful one is identified. It is a process that even the most dedicated clinicians struggle to navigate, and it can be overwhelming for families. This is why my work looks at how Artificial Intelligence (AI) can support and improve this process.

As a researcher working at the intersection of mental health, artificial intelligence, neuroimaging, and genomics, I’ve spent the past decade asking how we can improve. Can we make mental health interventions more precise and more inclusive? Can we move away from relying on observable behaviours and start using objective biological data to guide understanding, diagnosis, and treatment? And perhaps most importantly, can we ensure these improvements reach all children, not just a few?

I recently moved to King’s College London from the US-based National Institutes of Health to build a team focused on addressing exactly these questions. And while AI can’t fix everything, we’re seeing more and more ways it could meaningfully transform youth mental health care.

From symptoms to biology

Much of our current approach to mental health starts with symptoms—what a young person reports, how they behave, and how others describe them. That’s valuable, but it’s only part of the picture. Mental health conditions like attention-deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD) often vary widely between individuals: this variability isn’t captured by the diagnostic label. That’s where AI can help.

We’ve used machine learning to uncover brain-based subtypes—we sometimes call these “biotypes”—in large datasets of children living with ADHD. These subtypes reflect real differences in brain function that aren’t always visible through clinical observation. And they matter: we’re beginning to find that children with different biotypes may respond differently to different treatments.

For example, one subtype we identified involves disruptions in attention-related brain circuits, and those children tend to respond particularly well to stimulant medications. We’re now asking if other subtypes or biotypes might benefit more from non-stimulant medication or behavioural therapies. The idea is simple: tailor treatment to biology, not just behaviour. But the potential impact—faster recovery, fewer side effects, and less family stress—is enormous.

Predicting what comes next

Another area where AI is proving decisive is predicting outcomes. One of the most common—and essential—questions from children, young people, and families is about what the future might hold. This is a hard question to answer. Even detailed clinical assessments don’t reliably predict whether a child with a diagnosis of ADHD, for example, will continue to have troublesome symptoms into adolescence or will resolve.

That’s changing. In a recent project, we combined genetic and brain imaging data from children diagnosed with ADHD and trained a machine learning model to predict their future outcomes. With over 80% accuracy, we can now predict whether a child will meet diagnostic criteria in adolescence or whether symptoms will subside. That level of insight can be transformative for families and clinicians trying to make informed decisions about support and intervention. Our next critical step is to see if this predictive tool works in other settings.

Mind the gap

But with this exciting future comes a critical challenge: equity.

AI models are only as good as the data they’re trained on. And too often, that data doesn’t fully capture the children who need help the most—those from underrepresented backgrounds, or those with the most severe symptoms who can’t complete some research procedures (such as having an MRI scan).

If we’re not careful, we risk building tools that work well for some groups but leave others behind, reinforcing existing disparities in healthcare.

That’s why equity isn’t an afterthought in our research—it’s a design principle. In the new Pears Maudsley Centre, we’re investing in child-friendly, movement-tolerant brain imaging technologies that work even when kids are anxious or active. We’re also building strong partnerships with schools and communities to recruit participants who reflect the full diversity of the population. And we’re designing our models with inclusion in mind, so they’re more likely to benefit all children, not just the easiest to study.

A broader effort, a shared goal

This work is part of a broader effort across the King’s Maudsley Partnership, where researchers, clinicians, and technologists work together to improve youth mental health care. From digital tools being trialled in clinics to large-scale studies on how social media and cognitive functioning interact, we’re creating a more connected, data-informed understanding of mental health.

We’re also closely aligned with the new Pears Maudsley Centre for Children and Young People, which will bring cutting-edge research, clinical care, and community engagement under one roof. It’s precisely the kind of setting where research like ours can translate into real-world impact.