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Identifying and controlling infections is a global problem that causes health authorities billions of pounds per annum and has huge impacts on societies - COVID 19 is a prime example.

Within just hospital settings, pathogens such as C.Difficile, MRSA and E.Coli can cause hospital acquired infections (HAIs) due to various transmission pathways. Patients who become infectious can spread these pathogens to other patients, resulting in 5% of patients suffering a HAI.

In the UK alone, it is estimated that HAI patients occupy up to 20% of hospital beds and HAI’s lead to patients staying in hospital for an additional 9 days on average, with approximately 22,000 patients dying from HAIs every year. The estimated cost just to the NHS of patient overstays is estimated to be £23 billion per annum with the direct cost to treat HAI’s being estimated at over £2.8 billion per annum.

Proxximos has developed a propriety wearable solution that uses machine learning to identify, track and prevent HAIs in hospitals and beyond. When, a pathogen is detected, the company’s technology automatically produces an infection risk assessment and immediately and precisely identifies exposed persons and places to allow control measures to be deployed earlier and more accurately than existing technologies. Uniquely, the system learns from the difference between predicted and actual outcomes, so accuracy continuously improves.

The Proxximos approach has already shown success in a public health deployment on the Isle of Wight where, within 3 weeks of deployment, the R rate decreased from 1.3 to 0.3. Whilst hospitals are the company's initial focus, it is working on expanding the deployment of its technology to a number of healthcare systems and providers

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