- Led by King’s College London, in partnership with the Sentinel Stroke National Audit Programme (SSNAP), University College London and the University of Manchester.
- Will develop new tools to detect and interpret variation in stroke care quality and patient outcomes, and support better use of data for quality improvement in stroke care.
- Will integrate ‘machine learning’ algorithms into the national clinical audit for stroke.
- A 36-month project that will start in March 2018.
Translating the increasingly large and detailed amount of health care data into information that can be used to improve care quality is challenging. Current methods of audit and feedback often result in inconsistent and only modest effects on improvements.
Artificial intelligence, and in particular approaches based on ‘machine learning’, has developed rapidly over recent years. Machine learning involves the use of algorithms to detect and learn from patterns in data. These algorithms can identify otherwise hidden patterns in complex data. The technique is used in a variety of industries and settings, and is already being used in health care to automate disease diagnosis and for drug discovery.
This project led by King’s College London will use machine learning to support better use of data in order to improve the quality and consistency of care for stroke patients.
The project team will integrate machine learning into the Sentinel Stroke National Audit Programme (SSNAP), the national clinical audit of stroke care, which involves all acute stroke services in England, Wales and Northern Ireland.
This integration will produce more sophisticated analysis of SSNAP data, for use by SSNAP participants, such as clinicians, managers and commissioners.
Algorithms will be developed to provide more accurate predictions of patient outcomes after stroke; around survival, level of disability and risk of stroke-associated pneumonia.
Algorithms will also be developed to detect and classify the most important patterns of variation in care quality, in order to help SSNAP participants identify and understand variation within their local service.
The project will produce new knowledge about stroke care quality and outcomes, and will develop machine learning methods for use in audits in other clinical settings.
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For more information about this project, please, Research Director, SSNAP.
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