Supporting community care and reducing demand on A&E services
|Researchers involved:||Suzanne Mason, Maxine Kuczawski, Colin O’Keeffe, Richard Jacques, Jonathan Thornley, Susan Croft|
|Main disease areas impacted:||Urgent and emergency care|
|Key partner:||University of Sheffield|
There is increased demand on A&E departments across the UK. The services are becoming stretched and as a result waiting times are increasing and patient care is suffering.
By linking together patient data from different hospitals and services across Yorkshire, researchers are able to build a more complete picture of how accident and emergency services in the region function.
This picture will help researchers understand the flow of patients through A&E departments, to understand what the most common health issues are and to better plan community services in the future. The anonymous data can help scientists understand A&E services across an entire city and suggest improvements in a much more synchronised way.
Health service managers will also be able to understand how one A&E department in Yorkshire compares to another. By re-using existing data researchers will also allow hospitals to learn lessons from each other so that each local service can improve and deliver better care for its patients.
In the future, this information will help researchers to plan ahead and forecast disease outbreaks. The data used will, over time, tell a story that will help deliver better and more targeted care.
Why is this research project important?
The aim of the research project is to build a unique dataset based on expertise already being developed across the Yorkshire and Humber region. We will collect routine NHS data from a number of providers of emergency and urgent care (EUC) and link the data to provide a coherent picture of EUC demand. This rich data source will allow the EUC services to be viewed as a whole system, enabling demand on the system by patients to be analysed as well as the flow of patients through the system.
What data are being used in this project?
De-identified data will be collected and linked in de-identified form from numerous providers across the Yorkshire and Humber region including the Yorkshire Ambulance Service, NHS 111, NHS Hospital Trusts and out of hours services.
What methods are you using to conduct this work?
The individual datasets will be cleaned and linked using expertise already tested and established in The Collaboration for Leadership in Applied Health Research and Care, Yorkshire and Humber (CLAHRC YH) theme for Avoiding Attendance and Admission (AAA) in Long Term Conditions.
The large linked dataset will then be used to map the UEC system in Yorkshire and Humber, as well as understand :-
- Patterns of service use & outcome (mode of access, pathways of care) by different patient and demographic groups
- Groups of patients utilising services differently, who may benefit from an alternative approach to care
Use modelling techniques to design & test novel approaches to delivering care
Who will benefit from your research?
The linked dataset will directly benefit NHS organisations, commissioners, clinicians, researchers and NHS England, both in the Yorkshire and Humber region and nationally due to it being such a rich and unique source of information.
The outcomes of work by these individual organisations/ users will be used to identify areas within the UEC system that can be improved allowing targeted interventions, ultimately benefiting patients.
What will be the intended outcome of your research project?
There are numerous intended outcomes from the development of a large linked dataset, in the first instance the development and testing of interventions for managing patients attending the ED inappropriately. Analysis of the dataset will provide a greater understanding of the pathways leading to short term avoidable hospital admissions and how these patients managed avoiding the need for admission. Other intended outcomes will include:-
- Identifying improved pathways of care for vulnerable patient groups such as the frail elderly, acute mental health problems, social deprivation / isolation
Developing, modelling and testing hypotheses in order to inform their likely success and future research
Are there any early findings?
Initial data suggests that there are around 15% of adults attending the emergency department avoidably with conditions that could be easily managed elsewhere. These patients are more likely to attend out of hours, and be younger. Interventions to target at this patient group should be developed and tested in the future.