A recent Kings fund review highlighted how the National Health Service (NHS) is a significant contributor to public sector carbon emissions. The NHS accounts for 25 per cent of all public sector carbon emissions, or around 4 per cent of total emissions in England. This is greater than the annual emissions from all passenger aircraft departing from Heathrow airport. Current healthcare models are enormously inefficient with different specialties of healthcare providers siloed away from each other even within the same system; accounting for the vast waste of resources associated with routine care. The Kings fund report called for investment in new technologies to facilitate self-management as well as telecare and telehealth to drive the NHS to a sustainable future. The Academy of Medical Royal Colleges published a report in 2015 called Exercise the Miracle Cure, not only promoting the benefit of activity as a therapy in its own right, but also emphasising the dangers of inactivity. Those treated in ITU or elderly patients post-hip fracture will have the most severe form of "deconditioning syndrome", a side effect of their hospital admission and a consequence of inactivity. These patients require intensive rehabilitation regimes to regain their previous function and reduce their chance of ongoing health conditions associated with deconditioning. Current healthcare models require in person assessments from different speciality teams and continual follow-up clinic appointments. This leads to unnecessary travel, long waits between appointments and even multiple appointments from different departments which is all incredibly resource inefficient. Cush Health aims to develop a revolutionary remote monitoring healthcare platform for integrated care for patients and clinicians. Our solution allows remote monitoring through wearable devices to deliver assessment and treatment in the community when patients need it. It will function as a closed-loop system, whereby the clinical multi-disciplinary team(MDT) can review their patient population's progress in real time. They will be able to set individuals remote targets while deteriorating patients also be automatically highlighted to the relevant MDT members for review. This reduces travel and resources of the MDT and enables patient rehabilitation progression without waiting for follow-up appointments. By integrating into technology already widely disseminated in the community such as smartphones and smartwatches, this solution is scalable and can grow quickly to meet demand nationally leading the way towards net-zero in the public sector.

CBAS is proposing to develop a Prosthetic Interface Device (PID): Digital, an innovative, continuous, system that aids in collecting data remotely for patients with mobility impairments: patients with lower limb disorders and vulnerable elderly people. PID: Digital takes advantage of the CBAS machine-learning (ML) platform. This system is used to collect healthcare data from sensors worn by patients to enable remote assessment of their health. It provides clinicians with a true and complete picture of activity and mobility by representing patient conditions. This offers clinicians a clear tool to see that treatment is effective, progression of disease, and even clinical key performance indicators (treatment adherence/compliance measures). PID: Digital, can predict the need for in-house consultations with clinicians, potentially alleviating dependence on direct interaction between healthcare provider and patient, and supporting patient autonomy. The benefits include continuity of care, condition specific data, proactive intervention and reduced face-to-face assessment time via targeted patient engagement. This study will optimise existing ML algorithms for implementation in a cloud environment and build a system to scale these across multiple patients, clinicians and data types. These algorithms will provide clinically important information for identified patient groups, accessed via client end dashboards. All patients will be assessed in QMUL Gait Analysis Laboratory, providing gold standard validation. Clinical studies carried out by collaborators CUSH Health Ltd and Andiamo will trial PID: Digital alongside current best practise assessment methods. A regulatory and ethically compliant cloud environment and associated data storage will be designed and built with dashboards for identified for specified patients and associated user groups. The resulting system will be compliant to all medical device regulatory requirements to enable remote patient health assessment. On project completion, PID: Digital will have been trialled with two patient groups and be ready for regulatory submission as a class 1M medical device

We would like to carry out a 6 month feasibility study using remote patient monitoring to evaluate gait and balance in an elderly population. Out aim is to develop a machine learning tool to create a discrete healthcare wearable to prevent and protect against falls and hip fractures.