<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/A8F447B0-5F7F-4D5B-ACCC-77C6103C34B7" ns1:id="A8F447B0-5F7F-4D5B-ACCC-77C6103C34B7"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/65C32D7A-3459-4BF6-B8CA-D64E4ED466C8" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/D9DE1C85-416D-4EBE-A13A-43789E427C2A" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/D9DE1C85-416D-4EBE-A13A-43789E427C2A" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2021-02-28T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/09C9B328-E971-4E01-80BD-17A19AA4C3E7" ns1:rel="FUND" ns1:start="2019-05-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">105201</ns2:identifier></ns2:identifiers><ns2:title>AI-enabled geriatric platform for community health</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>&amp;quot;Globally there is a health workforce crisis, with a shortage of 7.2m healthcare professionals. For the NHS this equates to a 50,000 health workforce gap. These shortages result in restricted access to care, lack of personalised and regular assessment, and delayed identification of diseases and exacerbations. The consequences are most severe for the most vulnerable such as the elderly. In the UK, more than 9m people over 65 years live with at least one chronic condition.

Feebris is developing an AI-powered mobile health platform that enables non-medical users, such as carers and community health workers, to identify and monitor complex conditions in the community. This project will develop the geriatric application of the platform, focused on elderly people suffering from avoidable hospitalisations due to respiratory conditions, such as pneumonia, COPD and asthma. The Feebris platform will integrate rich &amp;amp; quantifiable health inputs, derived from diverse health sensors, to perform personalised evaluation of health risks. The AI-engine will process medical history and regular measurements to identify health trends (e.g. frailty) and combine multi-morbidity risk factors into prediction of complications. These advanced analytics will remove the need for a clinical taskforce to be continuously reviewing large volumes of data and instead focus resources on at-risk cases. Consequently, our innovation can both improve patient outcomes, as well as reduce pressures on health system resources.

The development will involve constructing a geriatric measurement system (off-the-shelf sensors &amp;amp; appropriate mobile app) and a unique AI-engine for diagnosis &amp;amp; personalised monitoring of respiratory ACSCs, capable of identifying health issues early to prevent complications. Additionally, the project will also ensure the platform is compatible with existing IT systems for health and care delivery, and compliant with highest regulatory standards for security, clinical performance and efficacy.

The project will be conducted in collaboration with clinical, engineering and business experts. Alongside the technical development of the platform, the project will also involve a clinical study; measurements collected during the study will ensure that the platform fits the needs of a variety of elderly people and can address their health challenges regardless of where they live.&amp;quot;</ns2:abstractText></ns2:project>