AI for early detection in employee mental health

人工智能可及早检测员工心理健康状况

• 批准号: 10044609
• 金额: $ 6.37万
• 依托单位: KAKTUS.AI LTD
• 依托单位国家: 英国
• 项目类别: Grant for 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=10044609
• 关键词: AI; early; detection; employee; mental

It is estimated that in the UK over 5 million workers suffer from mental health (MH) related problems. Mental health (MH) conditions cost the UK £34.9bn p/a in diagnosis and treatment, and an estimated £35bn in economic losses to the workforce. MH leaders, such as Mind, have indicated that early detection and holistic treatments would increase productivity whilst reducing pressures on society and the health service. Due to resource constraints, social taboos and often, fear and anxiety, patients are not self-identifying their symptoms early enough resulting in a "better late than never" last resort MH system. One of the leading causes of MH difficulties is work-related stress, anxiety, burn-out and unhealthy working environments. Following the impact of COVID-19 pandemic and on-going cost-of-living crisis, we can reasonably expect that social isolation will be a significant contributing factor to the rise of MH related issues within the UK population and in particular company employees. Most companies tend to use staff appraisal and internal feedback systems; however these are inadequate in identifying early onset of MH related issues.Our vision is to use artificial intelligence (AI), in the form of machine-learning (ML) with deep learning (DL) capability, to accurately detect early symptoms of MH challenges in the workforce and connect people who need more support with the appropriate form of responsive talking therapy to reduce the number of mild cases reaching the NHS. Early detection and intervention for mild MH conditions will relieve the NHS to focus on serious mental illnesses (SMI) which require medication or inpatient care, such as schizophrenia or addiction

据估计，在英国有超过500万的工人患有与精神健康相关的问题。精神健康（MH）状况在英国的诊断和治疗上花费了349亿英镑/年，估计劳动力的经济损失为350亿英镑。卫生保健机构的领导人，如Mind指出，早期发现和整体治疗将提高生产力，同时减少对社会和卫生服务的压力。由于资源限制、社会禁忌以及通常的恐惧和焦虑，患者没有足够早地自我识别自己的症状，导致“迟来总比不来好”的最后手段MH系统。卫生保健困难的主要原因之一是与工作有关的压力、焦虑、倦怠和不健康的工作环境。在2019冠状病毒病大流行和持续的生活成本危机的影响下，我们可以合理地预期，社会隔离将是导致英国人口，特别是公司员工中MH相关问题上升的一个重要因素。大多数公司倾向于使用员工评估和内部反馈系统；然而，这些在确定MH相关问题的早期发病方面是不够的。我们的愿景是利用具有深度学习（DL）能力的机器学习（ML）形式的人工智能（AI），准确检测劳动力中MH挑战的早期症状，并将需要更多支持的人与适当形式的反应性谈话治疗联系起来，以减少到达NHS的轻度病例数量。对轻度精神分裂症的早期发现和干预将使国民保健服务体系能够将重点放在需要药物治疗或住院治疗的严重精神疾病（SMI）上，如精神分裂症或成瘾