Smart Triage Data Box

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Milestone n:0%
What does each milestone mean?

    1 = Clinical need
    2 = Idea
    3 = Proof of concept
    4 = Proof of feasibility
    5 = Proof of value
    6 = Initial clinical trials
    7 = Validation of solution
    8 = Approval and launch
    9 = Clinical use
    10 = Standard of care


Our aims are to develop and systematically validate a framework that will cater to the provision of:

  • A major incident secondary triage tool
  • A “Silver Safety Net” tool and risk stratification of the older major trauma patient

Lay Summary

Clinical Triage aims to match the right patient with the right resources. Measuring the effectiveness of triage is usually done by examining the correct allocation of patients to levels of trauma facilities according to the Injury Severity Score. This approach has led to an over triage rate (patients being sent to an MTC facility) of approximately 34% and under triage rate of approximately 4%. The goal of the SmartTriage project is to address this issue by developing a state of the art major incident secondary triage framework as well as a risk stratification of the older trauma patient within the pre-hospital setting.


Trauma remains one of the leading causes of death and disability worldwide. It is the leading cause of death under 40 years of age in the UK. An increasing proportion of major trauma patients are aged 65 and over and this is due to rise in line with our ageing population. The purpose of triage is to reduce death and permanent disability by matching the needs of the injured patient to the most appropriate level of care. Controversies exist regarding the best way to identify severely injured patients in the routine pre-hospital setting, in the major incident setting and in those aged 65 and over.

The SMART Triage Group is working on developing a major incident secondary triage framework as well as a risk stratification of the older trauma patient within the pre-hospital setting.


In order to develop an MI Secondary triage tool, we integrate patient data from several points of the patient care pathway, from the pre-hospital phase to in-hospital treatment, in both the military and civilian setting. We will then build an AI-based framework for the triage as well as risk stratification of patients at different stages of admission which utilises machine learning methods, such as neural network and regression models, and combine it with prior medical knowledge.

We will also apply a similar approach develop models that predict clinical outcome in older trauma patients and in particular, evaluate the potential role of frailty scoring as part of the risk stratification of older patients suffering injury.  This will involve the amalgamation of data from the pre-hospital environment, clinical and demographic data collected at the regional level by TARN as well as in-hospital patient records at the University Hospital Birmingham NHS Trust.

Research Team

Dr Animesh Acharjee
Alina Bazarova
Damian Keene
Saisakul Chernbumroong

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