MonitorABLE - AI based Remote Patient Deterioration Detection Platform

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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.