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)，我们支持儿童精神疾病的能力存在危机。在年轻人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捉襟见肘，只向四分之一的有需要的人提供帮助，而且经常很晚才介入。及早发现和治疗是有益的，但可能会淹没服务，并造成更长的等待。一些年轻人由于耻辱(例如自我伤害)而不愿进入CAMHS。不平等也限制了机会(例如，那些经历经济困难或来自少数群体的人)。这些变异使许多人难以获得帮助，影响了他们的健康终身以及他们和他们家人的生活。我们需要重新思考如何提供CAMHS。使用数字工具使CAMHS更加公平和高效，可以帮助年轻人更快地获得正确的治疗。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)让年轻人获得对他们最有用的服务，而不是把每个人都送到CAMHS；或(3)帮助预测谁将从哪些治疗中受益最大，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，严重问题的风险由儿童早期经历(如欺凌、忽视、种族主义)、环境(如住房、饮食、居所附近绿地的数量)或身体因素(如遗传、炎症、脑化学)等因素的积累来表示。这样的数据已经从一系列来源收集，如产妇、健康访客、全科医生记录、学校和社会护理，但从未收集到一起。这些信息如果汇集在一起，就可以用来创建数字工具，使用人工智能(AI)来识别模式。然而，有一些问题需要首先解决。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，也不知道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，但可能不能确定社区中谁有患问题的风险，这可能是可以接受的。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组织，并利用这些数据来建立开发支持CAMHS的数字工具的最佳方式。然后，我们将与公众以及处理MH问题或有MH问题经验的专家合作，将人工智能算法转换为数字工具。这些数字工具必须是可以及早干预的临床服务的一部分。我们希望创建一种新的早期识别和预防服务，并确定需要哪些数字工具来使早期检测有效、安全和公平地工作。我们将把在学术界、工业界和临床上从事开创性工作的专家与政策制定者聚集在一起。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。数据被使用的人应该指导这些工具和新的临床路径的建立。我们需要他们的帮助，思考哪些数据应该用于什么目的，哪些人应该使用，当年轻人被认为出现MH问题时应该发生什么，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，为彻底改革效率低下的系统制定建议，并为未来的数据导向、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.