improving Service Access using Virtual Exposure (iSAVE)

对接受治疗的焦虑可以从短暂的担忧和消极情绪，到长期的特定恐惧症，对病人来说是一种使人衰弱的经历。避免治疗或诊断程序（如扫描）会对患者的护理途径和临床结果产生负面影响，并且已知避免引起焦虑的经历会在恶性循环中加剧问题。目前针对这一问题的解决方案（如治疗和镇静）往往昂贵、耗时且难以获得。iSAVE是一个使用虚拟暴露改进服务访问的项目。它将提供虚拟现实暴露治疗工具，以帮助那些对接受医疗保健治疗感到焦虑的患者。VR工具使用创新的眼动追踪硬件和软件，嵌入到VR头显中，监测患者在VR中的焦虑水平；这是现有曝光工具所没有的功能。机器学习用于根据患者的眼球运动来索引患者的焦虑水平，人工智能使虚拟工具能够智能地适应患者接受的暴露水平。这使得暴露体验对个人来说是个性化的，因此非常有效。iSAVE将进行更多的工业研究，使这一工具准备上市，并为需要它的患者提供。该项目将；(1)改进和实施作为该工具新功能的机器学习方法，并在焦虑患者身上进行测试；(2)进一步开发用户界面和体验，使患者和最终用户（从业人员和护理提供者）能够使用该工具；(3)确定未来需要哪些临床试验来减少在患者护理途径中实施的不确定性，包括对进入NHS的交付成分的分析，以及监管批准的要求。我们的目标是让iSAVE为心理健康生态系统做好准备。送到护理人员手中，以降低成本和节省时间；送到患者手中，以减少他们的焦虑并改善他们得到的护理。

Anxiety about receiving medical treatments can range from momentary worry and negative feelings, to longer term specified phobias and is a debilitating experience for a patient. Avoiding treatments or diagnostic procedures such as scans can negatively impact the care pathway and clinical outcomes for the patient, and avoidance of anxiety inducing experiences is known to exacerbate the problem in a vicious cycle. Current solutions to this problem for patients (e.g. therapy and sedation) are often expensive, time consuming and hard to access.iSAVE is a project which will improve Service Access using Virtual Exposure. It will deliver Virtual Reality exposure therapy tools to help patients who are anxious about receiving health care treatments. The VR tool uses innovative eye tracking hardware and software, embedded into the VR headset, which monitors the anxiety levels of the patients while they are in VR; something which has not been a feature of existing exposure tools. Machine Learning is used to index the patient's anxiety levels from their eye movements, Artificial Intelligence enables the virtual tool to intelligently adapt the level of exposure the patient receives. This makes the exposure experience personalised to the individual, and therefore highly effective.iSAVE will undertake additional industrial research that will make this tool ready for market, and accessible by the patients that need it. The project will; (1) improve and implement the machine learning approaches that are a novel feature of the tool, and test these with anxious patients; (2) further develop the user interface and experience to make the tool accessible to patients and end-users (practitioners and care providers); (3) establish what future clinical trials would be needed to reduce the uncertainty of implementation into the patient care pathway, including an analysis of delivery components into the NHS, and the requirements for regulatory approval.Our objective is to get iSAVE ready for the mental health ecosystem. Into the hands of care providers to reduce costs and save time, and into the hands of patients, to reduce their anxiety and improve the care they recieve.