Enhancing Type 2 Diabetes treatment through peer-learning AI: Creating a support system for healthcare professionals in primary care to source and share optimal self-care interventions and best practice in order to improve care and efficiency

**需求：**2型糖尿病的管理每年花费NHS 88亿GB，据估计，患者占当地手术水平所有预约的15%-25%。《全科医学前瞻性观点》确定，所有患有长期疾病的患者都应该有一个个性化的护理计划，其中包括自我护理、社交处方和主动签名，作为其5年内10项高影响力行动的一部分。人工智能(AI)等创新可以在这方面发挥关键作用--然而目前还没有市场就绪的AI系统来帮助医疗保健专业人员(HCP)在他们的时间压力下有效地完成这项工作。部分原因是从数字健康公司可用的海量患者数据中导出、机器教学(机器学习)、临床安全建议存在困难。**方法：**该项目将Healum有限公司(数字健康平台开发商)、Vernova CIC Foundation(国家医疗服务体系)、曼彻斯特大学(分包商)聚集在一起，开发一个对等学习AI和AI平台，通过个性化的护理、支持、行为改变和教育计划支持T2D患者的自我管理。与11家全科医生实践和SBRI支持(项目编号：8625456)合作，我们已经建立了方法的可行性，开发了一个原型协作自理平台和创新的同伴学习算法。**重点：**该项目的重点是Peer2Peer算法的改进和AI、机器学习(ML)算法、SnoMed代码集成、内容分类器和集体智能推荐引擎的开发，以及对平台在治疗2型糖尿病(T2D)中的匹配和评估。通过将HCP的集体Peer2 Peer智能输入到软件中，结合聚合匿名临床审计数据，我们能够减少训练ML算法所需的临界量数据输入，并以更高的数据置信度提供自动临床建议。**影响：**该平台将与新的SnoMed代码和历史读取代码集成并使用，以充当分类器-支持在正确的时间向正确的患者推荐正确的内容、服务和计划-并在项目期间与21个全科医生实践进行试验，以形成一个Peer2 Peer社区，为患有或面临T2 D风险的患者提供个性化的护理、支持、行为改变和教育计划。通过改进T2D护理工作负载的管理，我们估计，如果考虑到微血管并发症的减少，每年每一次实践节省成本的潜力为11,304 GB，到Y5将增加到61,254 GB。

**NEED:** The management of Type2 Diabetes costs the NHS £8.8bn per year with patients estimated to account for 15-25% of all appointments at a local surgery level.The 'General Practice Forward View' identifies that all patients with long-term conditions should have a personalised plan of care that includes self-care, social prescribing and active signposting as part of its 10 high impact actions within 5 years.Innovations such as Artificial Intelligence (AI) can play a key role in this - however at present there is no market-ready AI-system to assist Healthcare Professionals (HCP) to do this effectively within their time pressures, in part due to the difficulties associated with deriving, machine taught (machine learning), clinically-safe recommendations from the huge amounts of patient data available to digital health companies.**APPROACH:** The project brings together Healum Ltd (digital health platform developer), Vernova CIC Foundation (NHS), Manchester University (Subcontractor) to develop a peer2peer Learning AI and AI platform to support the self-management of T2D patients through personalised plans of care, support, behaviour change and education.Working with 11 GP Practices and with SBRI support (Project Number:8625456) we have already established feasibility of approach, developed an a prototype collaborative self-care platform and innovative peer-learning algorithm.**FOCUS:** The project's focuses on the refinement of peer2peer algorithms and development of AI, machine learning (ML) algorthims, SnoMed code integration, content classifier and collective intelligence recommendation engine as well as aligning and evaluating the platform in the treatment of Type2 Diabetes (T2D).By harnessing the collective peer2peer intelligence of HCP inputting into the software, combined with aggregate anonymised clinical audit data,we are able to reduce the critical mass of data inputs required to train ML algorithms and provide automatic clinical recommendations with a greater level of data confidence.**IMPACT:** The platform will be integrated with and make use of new SnoMed codes and historical Read codes to act as a classifier - supporting the recommendation of the right content, service and plan to the right patient at the right time - and trialled with 21 GP practices during the project to to form a peer2peer community for providing personalised plans of care, support, behaviour change and education to patients with or at risk of T2D.By improving the management of T2D care workload, we estimate potential for cost saving per practice per annum of £11,304, increasing to £61,254 by Y5 once reductions in microvascular complications are factored in.