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Details of Grant 

EPSRC Reference: EP/P01013X/1
Title: STRETCH: Socio-Technical Resilience for Enhancing Targeted Community Healthcare
Principal Investigator: Price, Professor B
Other Investigators:
Nuseibeh, Professor B Bandara, Professor AK Gooch, Dr D
Clare, Professor L Levine, Professor M
Researcher Co-Investigators:
Project Partners:
Age UK Milton Keynes Uni Hospital NHS Fdn Trust NHS NEW Devon CCG
St Thomas Medical Group Unit
Department: Faculty of Sci, Tech, Eng & Maths (STEM)
Organisation: The Open University
Scheme: Standard Research
Starts: 01 April 2017 Ends: 28 February 2021 Value (£): 1,049,532
EPSRC Research Topic Classifications:
Human-Computer Interactions Software Engineering
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Sep 2016 Intelligent Technologies Prioritisation Panel Announced
Summary on Grant Application Form
Treating older adults for medical conditions is complicated because they may need treatment for multiple conditions and they may also have chronic conditions such as reduced strength, mobility, hearing, eyesight or cognitive impairments such as dementia. This means hospital in-patient treatments may take longer and they may be unable to leave hospital to recover at home if they do not have a spouse/partner or family/friends/neighbours able to look after them. Older adults recovering at home frequently rely on 'circles of support' which range from relatives and neighbours, to the voluntary sector, social workers, paid carers, and medical professionals.

The STRETCH project aims to help coordinate these circles of support with both wearable and smart home technologies to enhance the social and technical resilience of these circles of support. This should have the double benefit of increasing NHS capacity to cope with increasing numbers of older patients while improving care by making sure that medical professionals have timely and accurate information at all times about their patients. By having intelligently combined both the human and sensor-based sources of data, physicians will be able to recognize when a patient is deteriorating and intervene early to pre-empt problems or longer hospital stays.

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