Durham planned and unplanned care pathway
a) The project delivered the necessary Information Governance Framework to enable data transfer from multiple NHS Trusts and GP practices, and adapted it for the new legislative environment. The documentation and process developed is already having impact elsewhere in the field and is currently being used by the team in another project. It is also being used by other researchers on another Health research project.
b) A planning tool was successful designed for Planned Care and developed in consultation and collaboration with General Practice (GP) managers. This was built, tested and developed using pseudonymised data and is now able to be successfully run using aggregated datasets. This simplifies the information governance procedures necessary and makes the app more easily accessible for practices.
c) The practice level app was developed to build a hub level model, combining datasets from multiple practices, which could inform service design across clusters of practices. This also works as a proof of concept for an area-wide app. The technical work has been completed to allow this to be run across the Primary Healthcare area, combining all of the practices, although this was not undertaken or tested.
d) This app is also being used by several practices to inform the next planning round. The team is still receiving requests to develop the app to help practices measure performance against key targets.
e) An unplanned care modelling app was developed to predict attendance at Accident and Emergency (A&E) departments. The A&E app was taken up by a Trust outside of the project.
f) The team procured an appropriate infrastructure to work securely with NHS data outside of the University’s own systems. The team worked with the provider to develop this solution and was able to pass the learning from this process on to the team designing the regional Trusted Research Environment.
g) The work carried out and relationships built, under this project, has led to further funded work. This new funded work is directly building on the work started under this exemplar project.
a) Agreement and accessibility of generic/template documentation would reduce the need for extensive institutional drafting or review each time.
b) Future projects may also, benefit from the provision of regional expertise from an organisation like AHSN or similar. It would allow for concentration of expertise in health data research and reduce dependency on individual organisations. This would avoid duplication of effort regionally, mitigate impact of changes and thereby minimise delays and ultimately costs.
c) Future projects should ensure that there is funding allocated for NHS Trusts Information Governance Support, which proved invaluable during this project.
d) CHC showcasing events were very useful in sharing the work that was going on between project teams. Future projects within a programme may benefit from coming together earlier in the programme to raise awareness of crossover and shared problems – which would improve collaborative working, problem solving and lead to efficiencies. It could also potentially generate further collaborations and grant applications.
e) Potential future work to develop a regional picture would need an NHS/external organisation to bring together support and champion the benefits. Great North Care Record (GNCR) developments could facilitate work of this type for future projects, providing a platform and establishing a trusted process.
f) Working with a consultant who has experience in the NHS organisations, and has the appropriate contacts and manages the links between the project team and collaborative partners from an external point of view has been key for this project. Future projects may well wish to consider replicating this model.
g) This project was funded to pay NHS Trusts for analyst time, which helped to keep the project on track. There was no funding to pay for datasets. Given the cost to NHS Trusts of collecting and maintaining this data, future projects may very well need to factor in data costs to the budget.