Computer Assisted Risk of Deterioration Score (CARDS)
Research type
Research Study
Full title
A study of the efficacy, implications and barriers to the future deployment of Computed Assisted Risk of Deterioration Score, a machine learning early warning score, in UK hospitals.
IRAS ID
282150
Contact name
Thomas WV Daniels
Contact email
Sponsor organisation
University Hospital Southampton NHS Foundation Trust
Duration of Study in the UK
3 years, 0 months, 0 days
Research summary
Hospitals collect data about patients to facilitate good care: to diagnose, assess, monitor, and personalise care. However, hospitals do not currently make maximum use of this data, and opportunities are being missed to improve care further. This study will look to use data that has already been collected as part of routine care to look for new patterns that may improve care further.
The research will extract clinical data from hospital systems and place it in a research environment (Wessex Secure Data Environment: SDE) that is under the control of the hospital. Researchers will “dial in” to the SDE to work on the data in a safe and secure environment.
The major focus will be looking at data to create an improved Early Warning Score (EWS) for detecting when patients are getting worse. Currently hospitals use National Early Warning Score v2 (NEWS2) which uses a snapshot of 7 vital signs. It does not learn over time, nor does it use other digitally available data such as blood results, pre-existing diagnoses, and demographic data. We will use machine learning (ML) techniques (already under development) to create a new EWS. However there are many questions that need answering before such a score could be used in clinical practice. The research will address many of these.
We will also study the health economic implications of an improved EWS for a health service. We will also address questions about the acceptability of ML to Health Care Professionals, and how best to present any new score to users.We will also study other aspects of care such as factors affecting length of stay, risk of readmission, and can they be predicted early during an admission with accuracy, which could have significant benefits for patient flow and thus the timely delivery of urgent care for new admissions.
REC name
Yorkshire & The Humber - Leeds East Research Ethics Committee
REC reference
24/YH/0228
Date of REC Opinion
5 Nov 2024
REC opinion
Further Information Favourable Opinion