Project

MH05 Timely: Towards early identification of young people’s mental health problems

Summary

Developing digital tools to identify young people’s mental health problems using linked health, social care and education data, and exploring the value of machine learning and artificial intelligence approaches. We are working with the public and those with lived experience to understand the acceptability of the approach and taking a systems’ thinking approach to understand how best to design and implement the tool to maximise uptake and impact on individuals.

Background

Mental health problems have significant negative effects on young people’s lives, and these often last into adulthood and adversely affect health, wellbeing, education, employment, and individuals’ relationships.  Intervening early can improve the lives of young people struggling with mental health problems. However, identifying problems is challenging, as the early symptoms often emerge  as other problems, such as not doing so well at school, struggling with friendships, or behaving differently. Early evidence suggests there may be some signs that can predict if and when a young person is developing problems. However, this information is held in a wide range of different places, such as the health records held by hospitals and GPs, or in information held by social workers and schools.

We will explore if it is possible to bring this information together in a safe and secure way to create a de-identified, ‘live’ data resource that links information from these sources. We will then see if it is possible to develop digital tools that can identify those with the early signs of a mental health problem, as well as those who are at high risk of developing one in the future. We will also explore if it is possible to predict which treatment would be most likely to help with young person. Using data like this has a huge potential to change lives for the better, but it is also incredibly important that information is only used in ways that are acceptable to the people concerned, so we will be exploring this in depth with young people and their carers.

Finally, to facilitate implementation and deliver impact to individuals we need to explore how early identification tools fit into current mental health services. So, we are working with engineers and taking a ‘Systems Thinking’ approach to understanding and developing child mental health services so we can understand how best to implement the tools in order to improve children’s outcomes. To address these challenges, we have created a multi-disciplinary collaboration drawing together expertise from the social sciences, public health, psychiatry, computer sciences, genetics, implementation scientists, artificial intelligence and engineering. Ensuring that we develop these tools and services to meet the needs of young people is critical to the success of this project. We are working with members of the public, those with lived experience of mental health problems and the Anna Freud National Centre for Children and Families, all of whom have a formal role to develop and run the programme, as well as contribute to the generation of our research.

Project aims

During the year we will:

  • Create a collaboration of experts from academia, clinical services, education, social care, computer science, data security, and policy makers, who will work together to try to solve some of the challenges of building a digital early intervention tool.
  • Explore with young people, their families and carers, and those who have experienced mental health problems in childhood whether they think a tool like this would be a good idea, how they feel about data being used in this way, and how they think a tool like this should be used.
  • Determine how best to achieve the high levels of data security required, so that only those people involved in a person’s care can see identifiable information. We want to ensure that we establish  how  to build a pilot dataset of de-identified information and test if it’s possible to create a ‘real-time’ resource with which to build our early identification tool.
  • Explore how to make a digital tool work within the NHS, schools, and social care settings to enable earlier identification of young sufferers of mental health problems. 

Project activity

The project has four workstreams:

  1. PPI and acceptability – we have held our first workshop and feedback session which has helped shape our protocol and ethics application. Identified individuals to join the core team.
  2. Technical build - we have submitted ethics application to create pilot linked dataset. Undertaking a pilot to translate Intermine into database for healthcare use using de-identified CAMHS health service data.
  3. Data curation and analytics - established clinical and data leads at each site and are currently developing data requests with the four partners sites (CUH, CCS, Local authority and CPFT). Planning a scoping review and Delphi process to prioritise data requirements.
  4. Systems thinking – completed stakeholder mapping exercise and the ‘Improvement Canvas’ to plan systems thinking approach.

Potential or actual impact

  1. Digital tool that identifies children and young people with early stages of MH problems, and those at risk of future problems.
  2. Implemented into re-designed clinical pathways which incorporate the tool.
  3. Training materials for professionals.
  4. Validated artificial intelligence algorithms that can be utilised in other clinical information systems.

Papers/resources 

  1. Linked seven-year cohort of de-identified information relating to individuals 0-18y in Cambridge & Peterborough.
  2. Live linked databased of identifiable information relating to CYP 0-25y in Cambridge & Peterborough, for clinical use.
  3. Live linked de-identified database  of de-identified information relating to CYP 0-25y in Cambridge & Peterborough, for population health and research use.
  4. No publications as yet.

Next steps

PPI & Acceptability: we will co-design the acceptability research programme with PPI group & Anna Freud Centre.

Data curation: plan Delphi and scoping review, submit ethics.

Technical build: hold first IS leads meeting in early Nov and plan pseudonymisation approach using CRATE.

Systems thinking: we need to plan the workstream for this.

Who is involved?

PI Dr Anna Moore

Researchers and institutions

Dr Rudolf Cardinal, Psychiatry, University of Cambridge

Professor Tamsin Ford, Child and Adolescent Psychiatry, University of Cambridge

Professor John Clarkson, Engineering, University of Cambridge

Dr Gos Micklem, University of Cambridge, Genetics

Mr Tony Evans, Transformation, Huntingdonshire District Council

Professor Peter Fonagy, Division of PALS, University College London

Professor Julian Edbrooke-Childs, UCL and Anna Freud National Centre for Children and Families

Professor Pietro Lio, Computer Science, University of Cambridge

Professor Peter Jones, Psychiatry, University of Cambridge

Professor David Henry Rowitch, Paediatrics, University of Cambridge

Professor Anna Vignoles, Faculty of Education, University of Cambridge

Professor Carol Brayne, Public Health and Primary Care, University of Cambridge Dr Robbie Duschinsky, Public Health and Primary Care, University of Cambridge

Contact

Anna Moore

Am2708@medschl.cam.ac.uk