• Run by Nottingham University Hospitals NHS Trust, in partnership with the East Midlands Academic Health Science Network (EHAHSN).
  • Aiming to implement a system-wide early warning tool for acute pathways that will allow better response planning.
  • Advanced data analysis techniques will be used to ensure robust operational decisions can be made.
  • Will run from September 2018 to August 2019.

Recent winters have shown the huge pressure on health care systems and the challenges there are with managing emergency admissions.

This project led by Nottingham University Hospitals NHS Trust is aiming to demonstrate how expert analysis of large and multiple data sources can be used to support operational decision making in acute care.

It has been shown that clinical observations recorded in emergency departments, combined with other data, can predict pressure to discharge patients, and much of these data are available in real-time. This provides an opportunity to create a live demand prediction model, which would enable both the Trust and its partners to plan responses in the short term, rather than requiring an urgent response once the intense pressure to discharge hits the system.

This project will explore how ‘actionable intelligence’ (using information collected as part of routine care to ensure services respond to patient needs) can be used to allow a more considered and timely response across the health economy, for example being able to deploy staff differently or planning for increased community support.

As a result of this project, Trust operational staff will be able to take action in advance that will reduce delays and waste; reduce lengths of stay, bed occupancy and re-admissions; improve the efficiency of care processes; and improve quality of care.

Once the model has been developed and tested, it will provide a template that could be adopted across other NHS Trusts. 

Contact information

For more information on this project, contact Glen Howard, Programme Manager, Health Analytics and Informatics, EMAHSN.

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