MonitorABLE - AI based Remote Patient Deterioration Detection Platform

随着急诊室就诊人数和入院人数的增加，以及**预算压力、**积压和日益**老龄化、健康状况不佳的人口**，世界各地的**NHS和医疗系统都承受着巨大的压力。医院人满为患。然而，每5个**紧急入院中就有1个是**可避免的**，占NHS所有医院床位的13%，每年花费**25亿GB**，其中**40%的死亡**是**可预防的**原因。考虑到当前医疗预算和更广泛的经济压力，增加工作人员不可能是唯一的解决方案--**相当于8800名全职全科医生和6400名护士职位到2030年将空缺**。在这种背景下，NHS英格兰的长期计划承认数字解决方案是必不可少的。**全科医生手术负责监测患者**，如果他们有特定的风险因素，每年对他们进行一次或多次审查。然而，在这些预约之间，患者在身体不适到需要紧急护理之前，通常会在几周或几个月内**微妙地、逐渐地、通常没有察觉地恶化**。一种**可穿戴设备本可以**及早检测到这些信号，从而进行评估、治疗并避免病情恶化。远程监测数据通过基于机器学习的技术数据进行处理，为**在家中有**健康恶化**无声信号的**患者创造了**早期检测系统的机会。该项目将允许**更好地针对现有的预防和监控**活动，并更有效和高效地利用现有的临床能力，**支持医疗系统_预防住院，**在减少医疗支出的同时伤害患者。**可穿戴和远程监控设备越来越多地被患者使用，包括智能手表(15%)、健身跟踪器(21%)和带有步长计数器的智能手机(90%)**。此外，10名患者中有6名会同意与他们的医生分享这一数据。所有关于患者健康状况和他们计划的预约的相关信息都将存储在电子健康记录(EHR)中。这个项目将提供一个系统，它**结合了全科医生目前可用的数据**和使用现有设备从家里的患者那里收集的额外**数据**。该系统将识别患者并确定他们的优先顺序，建议修改他们的常规监测时间表，使全科医生能够通过带他们来进行更紧急的预约，及早对怀疑病情恶化的患者做出反应。预计每年的预约总数将保持不变，但在丰富的数据源和先进的数据处理技术的指导下，患者将在合适的时间被引入。

The **NHS and health systems around the world are under great pressure** with rising A&E attendances and hospital admissions alongside **budgetary pressures,** backlogs and an increasingly **ageing, less healthy population**.Hospitals are overflowing. However, **1 out of 5** emergency admissions are **avoidable** and responsible for taking up 13% of all NHS hospital beds, costing **£2.5 billion each year** with **40% of all deaths** having **preventable** causes.Given the current stresses on the healthcare budget and wider economy, increasing staff cannot be the only solution - **the equivalent of 8800 full-time GPs and 6400 Nurse posts will be vacant by 2030**. In this context, digital solutions are recognised to be essential by NHS England's Long-term Plan.**GP surgeries are responsible for monitoring patients**, reviewing them once or more each year if they have certain risk factors. However, in between these appointments, patients often have **subtle, gradual, often unrecognised worsening** over weeks or months before becoming unwell enough to require urgent care. A **wearable device could have picked up these signals** early, allowing assessment, treatment and avoiding deterioration.Remote Monitoring data, processed through machine learning based technology data creates an opportunity to build **an early-detection system for** patients who are at home with silent signals of **worsening health**. This project would allow **better targeting of _existing_ prevention and monitoring** activities and uses existing clinical capacity more effectively and efficiently, **enabling health systems _prevent_ hospitalisations,** patient harm whilst reducing health spending.**Wearable and remote monitoring devices are increasingly commonly used by patients including smartwatches (15%), fitness trackers (21%), and smartphones with step counters (90%)**. Furthermore, 6 out of 10 patients would agree to share this data with their doctor. All relevant information about a patient's health condition and their planned appointments will be in electronic health records (EHRs).This project will deliver a system which **combines data currently available to the GP** with additional **data collected from patients at home** using existing devices they might already have to.The system will identify and prioritise patients, suggesting modifications to their routine monitoring schedule allowing the GP to react early which a patient is suspected to be deteriorating by bringing them in for a more urgent appointment. The overall number of annual appointments is expected to be the same, but patients will be brought in at the right time for them, guided by rich data sources and advanced data processing techniques.