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，哮喘患者将达到4亿人，每天有1000人死亡。英国是欧洲患病率和死亡率最高的国家之一。在英国，超过540万人患有这种复杂的疾病，通常很难管理。在英国，每年约有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，成本就超过了1.1B GB。不受控制的哮喘仍然是一个顽固的难题。在过去的20年里，尽管制药公司对新药和昂贵的药物输送技术进行了重大投资，但结果一直停滞不前，计量吸入器(MDI)仍然是治疗的主要手段。吸入器技术不正确和依从性差可能会对吸入器的症状控制产生不利影响。创新主要集中在应用程序和“智能吸入器”上，这些应用程序和“智能吸入器”会促使人们遵守(而不是吸入技术)，并监测吸入器的使用情况，以改善症状控制。这些第一代智能吸入器的益处证据有限，审查表明，第二代智能吸入器需要提供临床、生理和行为数据洞察，包括识别吸入技术和环境相互作用，以改进自我管理，确保最佳症状控制。最新的智能吸入器和平台提供了一些这一功能，但未能识别、集成和纠正正确吸气的关键步骤，以支持自我管理。基于之前制造CE标志的吸入器的专业知识，我们的计划将首次提供每个吸入器步骤的详细、实时可视化。_我们的研发将提供基于云的、人工智能驱动的数据聚合和分析平台，以允许向新的App和医疗保健专业人员(HCP)仪表板实时报告，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聚合的临床数据，空气质量测量、天气和流行病学数据与临床专业知识相结合，将通过数据洞察支持超个性化的自我管理，并识别用于HCP干预的“高危”患者。向用户和HCP提供这些实时数据洞察将减少可避免的HCP就诊和住院的成本负担。在整个研究期间，我们将与英国哮喘患者、临床医生和用户焦点小组密切合作，以迭代的方式改进产品和应用程序的设计，以确保用户可接受。

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.