FAIR TREATMENT: Federated analytics and AI Research across TREs for AdolescenT MENTal health

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Negative aspects of a young person's life can lead to poor mental health (MH). However, services are stretched so often intervene late, leaving young people to suffer with longer lasting / more severe problems. It is possible to spot patterns showing who needs professional help early. However this is difficult as the information needed is secured in different places (e.g.health, education, social care records and falls under the remit of different research councils (MRC, ESRC). The main problems are:1) predictive models aren’t accurate enough: difficulties linking the above data together probably result in many factors being missed;2) models built in one place may not be effective in others: we need a way to securely analyse data from different places;3) there is no agreement on how to make sure data are managed safely, fairly and transparently. To solve these problems we will:1) combine two new technologies to demonstrate it is possible to analyse data across trusted research environments in different places and preserve individual’s privacy;2) consult with patients, the public, organisations contributing data, and legal/ethics experts to agree the best way to oversee data use, ensuring it’s managed safely and fairly.We can start quickly as we have been working together for three years and have already been funded to bring data together from education, social care and health services in Cambridgeshire and Peterborough, and the necessary ethical permissions are in place.