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eGRiST Mental Health Risk Assessment and Safety Technology

eGRiST 心理健康风险评估和安全技术

基本信息

批准号：
10018501
负责人：
金额：
$ 37.86万
依托单位：
EGRIST LTD
依托单位国家：
英国
项目类别：
Collaborative R&D
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=10018501
关键词：
eGRiST Mental Health Risk Assessment

项目摘要

One-in-six adults will have suffered mental health problems in the past week. Mental health carries an economic and social cost of £105bn/annum, with suicide the leading cause of death for those between 10 and 34 years. There are many vulnerable people needing mental health assessments, but they require trained people to carry them out and the data collected is not easily analysed or shared. Even practitioners ?nd it dif?cult to create meaningful management plans from their assessments and patients do not feel that they have much of a say.Aston University has been researching these issues for more than a decade. It has developed a web-based questionnaire, GRiST, which models the way practitioners think and reason about mental health and associated risks of suicide, self-harm, harm to others, vulnerability, and self-neglect. GRiST breaks down each risk concept into simple questions that help assessors work out a person's risk level from 0 (minimal risk) to 10 (maximum risk).GRiST has been adopted by several NHS Trusts and other mental-health services over the years and has built up a unique data set of 1.5 million risk assessments. Each one selects a person's relevant subset of answers from 300 precisely-quanti?ed questions and links them to a speci?c risk level. EGRIST Ltd was spun out of Aston to capitalise on this technology.Our proposal is to develop an automated, digital, structured decision support system known as eGRiST, that can predict levels of mental-health risks and provide advice on how to manage them safely. Innovation arises from the combination of its intuitive psychological model for representing mental health expertise and the development of appropriate arti?cial intelligence and machine learning algorithms. These will improve the accuracy of risk evaluations of assessors by benchmarking them against their colleagues and help them generate more effective plans for reducing risks, both in the immediate and longer term. The expertise will also be shared with patients through self-assessment technology, so that patients can understand and manage their own mental health in collaboration with their clinical team. The resulting "canopy of care" will deliver reliable, safe, and effective mental-health support for anyone, wherever they are, at the click of a button. It will streamline mental-health services by more accurate referrals, improve communication between patients and practitioners, and give people more control over their own mental health.

在过去的一周里，六分之一的成年人将遭受心理健康问题。心理健康每年带来1050亿英镑的经济和社会成本，自杀是10至34岁人群的主要死亡原因。有许多弱势群体需要进行心理健康评估，但需要训练有素的人员进行评估，而且收集的数据不易分析或共享。连修炼者也是？那就不一样了邪教创造有意义的管理计划，从他们的评估和病人不觉得他们有太多的发言权。阿斯顿大学一直在研究这些问题超过十年。它开发了一个基于网络的问卷，GRiST，它模拟了从业者对心理健康以及自杀，自我伤害，伤害他人，脆弱性和自我忽视的相关风险的思考和推理方式。GRiST将每个风险概念分解为简单的问题，帮助评估人员计算出一个人的风险水平，从0（最小风险）到10（最大风险）。多年来，GRiST已被几家NHS信托基金和其他心理健康服务机构采用，并建立了一个独特的150万风险评估数据集。每一个选择一个人的相关子集的答案从300精确定量？艾德的问题，并将它们链接到一个特定的？c风险水平。EGRIST有限公司是从阿斯顿分拆出来的，旨在利用这项技术。我们的建议是开发一个自动化、数字化、结构化的决策支持系统，称为eGRiST，它可以预测心理健康风险的水平，并就如何安全地管理这些风险提供建议。创新产生于其直观的心理模型，代表心理健康的专业知识和适当的阿尔蒂的发展相结合？人工智能和机器学习算法。这将提高评估人员风险评估的准确性，将他们与同事进行比较，并帮助他们制定更有效的计划，以减少近期和长期的风险。这些专业知识也将通过自我评估技术与患者分享，以便患者能够与临床团队合作了解和管理自己的心理健康。由此产生的“护理之冠”将为任何人提供可靠，安全和有效的心理健康支持，无论他们在哪里，只需点击按钮。它将通过更准确的转诊来简化心理健康服务，改善患者和医生之间的沟通，并让人们更好地控制自己的心理健康。