AI driven inhalation technique and adherence support device with data aggregation platform for remote monitoring of asthma

全球哮喘的成本很高，而且还在上升，预计到2025年将有4亿患者，每天有1000人死亡。英国是欧洲发病率和死亡率最高的国家之一。英国有超过540万人患有这种复杂的疾病，通常很难控制。在英国，每年大约有1400人死亡，其中高达70%的死亡是可以通过更好的症状控制来预防的。仅在英国，成本就超过11亿英镑。不受控制的哮喘仍然是一个顽固的棘手问题。尽管制药公司对新药和昂贵的给药技术进行了大量投资，计量吸入器（MDIs）仍然是治疗的主要手段，但在过去20年里，治疗结果趋于稳定。不正确的吸入器技术和不良的依从性会对吸入器设备的症状控制产生不利影响。创新集中在应用程序和“智能吸入器”上，这些应用程序和“智能吸入器”可以促进依从性（而不是吸入技术），并监测吸入器的使用情况，以试图改善症状控制。这些第一代智能吸入器的益处证据有限，评论指出需要第二代来提供临床，生理和行为数据见解，包括吸入技术和环境相互作用的识别，以改善自我管理并确保最佳症状控制。最新的智能吸入器和平台提供了部分功能，但无法识别、整合和纠正正确吸入的关键步骤，以支持自我管理。基于之前制造CE标志吸入装置所获得的专业知识，我们的提案将首次提供每个吸入步骤的详细、实时可视化。_我们的研发将提供一个基于云的、人工智能驱动的数据聚合和分析平台，允许实时报告到一个新的应用程序和医疗保健专业人员（HCP）仪表板，生成风险通知并在合成人群中进行测试，为英国临床试验做好准备。将机器学习算法应用于汇总的临床数据、空气质量测量、天气和流行病学数据，结合临床专业知识，将通过数据洞察支持超个性化的自我管理，并识别“有风险”的HCP患者进行干预。向用户和HCP提供这些实时数据见解将减少可避免的HCP访问和住院的成本负担。在整个研究期间，我们将与Asthma UK、临床医生和用户焦点小组密切合作，以迭代的方式完善产品和应用程序的设计，以确保用户的可接受性。

The global cost of asthma is high and rising with an expected 400M sufferers by 2025 and 1000 deaths every day. The UK has one of the highest prevalence and mortality rates in Europe. Over 5.4 million people in the UK have this complex condition which is often difficult to manage. Around 1400 people die each year in the UK and up to 70% of these deaths are preventable with better symptom control. In the UK alone costs are over £1.1B.Uncontrolled asthma remains a stubbornly intractable problem. Outcomes have plateaued in the last 20 years despite major pharma investment in new drugs and costly drug delivery technologies and Metered Dose Inhalers (MDIs) remain the mainstay of treatment. Symptom control by inhaler devices can be adversely impacted by incorrect inhaler technique and poor adherence. Innovation has focused on Apps and 'smart inhalers' that prompt adherence (not inhalation technique), and monitor inhaler usage to try to improve symptom control.Evidence of benefit from these first generation smart inhalers has been limited and reviews point to the need for a second generation to deliver clinical, physiological and behavioural data insights including the identification of inhalation technique and environmental interactions, to improve self management and ensure optimum symptom control. The latest smart inhalers and platforms deliver _some_ of this functionality but fail to identify, integrate and correct the critical steps of correct inhalation to support self management.Building on the expertise gained building a previous CE marked inhalation device, our proposal will give detailed, real-time visualisation of each inhalation step, _for the first time._ Our R&D will deliver a cloud based, AI driven data aggregation and analytics platform to permit real time reporting to a new App and healthcare professional (HCP) dashboard, generate risk notifications and test in a synthetic population, ready for UK clinical trials.The application of Machine Learning algorithms to the aggregated clinical data, air quality measurements, weather and epidemiology data, combined with clinical expertise, will support hyper personalised self management via data insights and identify 'at risk' patients for HCP intervention. Delivering these real-time data insights to users and HCPs will reduce the cost burden of avoidable HCP visits and hospital admissions.Throughout the duration of the research we will work with closely with Asthma UK, clinicians and user focus groups to refine the design of the product and the app in an iterative way, to ensure user acceptability.