Transforming child mental health: co-designing, building and evaluating a digitally enabled, personalised, prevention pathway

年轻人生活的许多方面都会影响他们的心理健康（MH），我们支持儿童心理疾病的能力出现了危机。在年轻人能够获得儿童和青少年心理健康服务（CAMHS）之前，问题往往必须变得严重。CAMHS已经捉襟见肘，只能为四分之一的需要帮助的人提供帮助，而且往往干预得很晚。早期识别和治疗是有益的，但可能会使服务陷入困境，并造成更长的等待时间。一些年轻人因为耻辱感（例如自残）而不愿使用CAMHS。不平等也限制了获取机会（例如经济困难的人或少数群体）。这些差异使许多人难以获得帮助，影响了他们一生的健康以及他们及其家人的生活。我们需要重新思考如何提供CAMHS。使用数字工具使CAMHS更公平、更有效，可以帮助年轻人更快得到正确的治疗。例如，应用程序或网站可以用来：(1)在某人需要强化治疗之前及早发现问题，(2)为年轻人指明对他们最有用的服务，而不是把每个人都送到CAMHS，或者(3)帮助预测谁将从哪种治疗中获益最多，这样年轻人就能第一时间得到正确的治疗。这可以通过利用“大数据”的力量来实现。关于年轻人生活的信息（数据）可能会有所帮助。例如，出现严重问题的风险可以通过各种因素的积累来显示，如早期儿童经历（如欺凌、忽视、种族主义）、环境（如住房、饮食、家附近的绿地数量）或物理因素（如遗传、炎症、脑化学）。像这样的数据已经从一系列来源收集，如产科、健康访客、全科医生记录、学校和社会保健，但从未汇集在一起。这些信息如果汇集在一起，可以用来创建使用人工智能（AI）识别模式的数字工具。然而，有一些问题需要先解决。我们不知道哪些数据最有用，如何最好地将数据安全地整合在一起，或者最有效的人工智能方法。重要的是，我们还没有就哪些信息应该用于哪些目的达成一致。例如，在医院使用遗传信息来决定哪种药物是最安全的，这可能是可以接受的，但在社区中识别谁有患某种疾病的风险，可能就不合适了。我们必须把这件事做好。在这项研究中，我们将从广泛的来源获取数据，其中一些我们将在项目的早期阶段收集和整理，并利用它来建立开发数字工具以支持CAMHS的最佳方法。然后，我们将与公众以及与MH问题打交道或有相关经验的专家合作，将人工智能算法转化为数字工具。这些数字工具必须是临床服务的一部分，可以早期干预。我们希望创建一种新的早期识别和预防服务，并建立所需的数字工具，使早期检测工作有效、安全、公平。我们将把在学术界、工业界和诊所从事开创性工作的专家与决策者聚集在一起。我们希望将他们的注意力转移到解决这些问题上，与年轻人、他们的照顾者和有生活经验的人一起。使用数据的人应该指导这些工具和新的临床途径的建立。我们需要他们的帮助，思考哪些数据应该用于什么目的，针对哪些人，当一个年轻人被认为有MH问题时应该怎么做，以及如何使用数字工具来支持治疗决策。在以后的几年里，我们将探索早期识别和预防方法的有效性，为改革效率低下的系统提出建议，并为未来的数据指导、个性化和及时的卫生保健干预制定模板。

(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.