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SLAIDER (Self-Learning AI-based Digital twins for accelerating clinical care in Emergency Respiratory admissions)
Safe People
Organisation name
University of Leicester
Organisation sector
Academic Institute
Applicant name(s)
Dr Robert Free
Funders/ Sponsors
University of Leicester
Safe Projects
Project ID
SDE_EM_PROJ_0007
Lay summary
Respiratory-related hospital admissions remain a major concern in the UK. In England alone, there were over 200,000 emergency hospital admissions in 2021/22. The effect of this is most apparent during winter, when respiratory-related admissions double due to 'winter pressures', leading to an overloaded health service and preventable deaths. Our prior research has led to the development of a proof-of-concept clinical decision support tool (SLAIDER) based on a concept known as 'digital twins'. Our Artificial Intelligence (AI) based approach can predict a patient's future state at any point in the patient's stay using both historical and real-time data. Allowing earlier prioritisation and clinical interventions. We aim to evaluate and improve SLAIDER using data from multiple hospitals to test the proof-of- concept technology and identify weaknesses, then use these findings to make targeted improvements to solve these problems.
Public benefit statement
This innovative Artificial Intelligence (AI) based approach can predict a patient's future state at any point in the patient's stay using both historical and real-time data. This will allow earlier prioritisation and clinical interventions – leading to improved patient outcomes, particularly in patients who are initially admitted with low or moderate severity conditions.
Request category type
Public Health Research
Other approval committees
Project start date
01/10/2025
Project end date
30/09/2026
Latest approval date
19/09/2025
Safe Data
Dataset(s) name
Custom Secondary Care
Data sensitivity level
Anonymous
Legal basis for provision of data under Article 6
Not applicable
Lawful conditions for provision of data under Article 9
Not applicable
Common Law Duty of Confidentiality
Not applicable
National data opt-out applied?
Yes
Request frequency
One-off
Safe Setting
Access type
TRE
How has data been processed to enhance privacy?
Anonymisation, no postcodes