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  • Run by Barking, Havering and Redbridge University Hospitals NHS Trust, in partnership with the The Alan Turing Institute, the University of Warwick and the University College London Institute of Digital Health.
  • Explored how best to assist the emergency department triage process using advanced health analytics.
  • Developed, tested and implemented a digital tool that helps nurses to safely triage the most high-risk patients.
  • Ran from September 2018 to October 2019.

Hospital emergency departments are high-pressure environments and there is high clinical uncertainty. Health care staff screen enormous numbers of patients daily in order to assess illness severity and identify high-risk patients needing urgent care.

The goal of this project from Barking, Havering and Redbridge University Hospitals NHS Trust was to assist this critical triage process by using advanced health analytics to quickly and accurately identify high-risk patients.

The Trust’s health analysis team had developed a dashboard that displays patient flow data in real-time. This project involved augmenting this with patient-specific data that can guide the decisions of managers and clinical staff as soon as patients arrive at triage. This was an NHS academic collaboration with the Healthcare Data Study Group Workshop hosted by the Alan Turing Institute.

Data was drawn from medical records obtained during triage at the emergency department at the Queen’s Hospital in Essex over the last five years, as well as other electronic health records held within the hospital. A dedicated server was set up so that the exchange of data could operate in real-time. Time was spent cleaning the data – the cleaning algorithms used have been published so that other trusts can use them.

Various algorithms were tested to assess prediction probability of admission for various patient groups, to understand if three main pathologies (stroke, heart attack and sepsis) could be identified early by generating personalised risk scores. A web app was co-designed with nurses and a group of nurses were trained in the system.

The tool improves the efficiency and safety of triage by reducing uncertainty, facilitating early life-saving interventions and reducing the risk of long waiting times for patients needing urgent care.  

Contact information

For more information about this project, please contact Nik Haliasos, Digital Transformation Lead and Consultant Neurosurgeon, Barking, Havering and Redbridge University Hospitals NHS Trust.

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