- Will assess the feasibility of linking patient-level data on healthcare utilisation following a telephone call to NHS 111 in three London CCGs for children with common complaints that have NICE guidance associated with them (fever, diarrhoea, constipation and breathlessness).
- Team hope to gain insight into how parents/carers of children comply with NHS 111 advice and what factors are associated with their decision-making and behaviour.
- Offers an important opportunity to improve clinical outcomes among children and young people, at the same time as tackling the expensive and unsustainable rise in A&E attendances and short-stay admissions among young children.
- Final report released in Summer 2015, further research is ongoing.
The data analytics team at The Health Foundation aims to be creative about the use of patient health records and other large scale datasets (‘big data’), while working at the intersection of healthcare delivery, policy analysis and methodology. Increased integration and analysis of medical and administrative datasets, including linking health patterns and factors such as education, environment and socio-economic status, may provide valuable insight that can improve the understanding and management of the population’s health.
Funded by the NHS 111 Learning & Development Programme, we are working collaboratively with the Royal College of Paediatrics and Child Health (RCPCH), University College London (Institute of Child Health; Dr Dougal Hargreaves) and Imperial College Healthcare Trust (Dr Ian Maconochie) to assess the feasibility and reliability of linking individual data on NHS 111 calls to routinely-collected administrative data on activity in other NHS settings. After successful extraction and linkage of the pseudonymised data (stored on our secure environment), we will begin characterising the children who were the subjects of the NHS 111 calls. This will be based on information available within the calls and of records from previous hospital (and ideally GP) contact.
A final report for this project was released in Summer 2015 and further research is ongoing.
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