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A clinical prediction model to risk stratify children at Paediatric ED triage

24 months
Approved budget:
Dr Zhenqiang Wu
Dr Laura Chapman
Associate Professor Janak de Zoysa
Associate Professor Stephen Howie
Professor Robert Scragg
Dr Katherine Bloomfield
Dr Joanna Hikaka
Proposal type:
Research Development Award
Lay summary
Triage is vital to manage patients in Emergency Departments (EDs). Triaging for children is more challenging than for adults, as special knowledge and communication skills are necessary to assess their urgency accurately. There are risks that children might be either “over-triaged” or “under-triaged”. Under-triaging can cause adverse outcomes. Over-triaging runs the risk of unnecessary intervention. Inequities in health system are associated with poor outcomes in tamariki Māori, and one possible cause is systematic under-triaging that deserves investigation. This study will examine healthcare inequity in ED triage and outcomes and their associated factors; and develop prediction models to help nurses better assess a child’s illness using routinely collected ‘big-data’. Such a data-driven tool would have the following benefits: early identification of children who are likely to become seriously sick, leading to better triage decisions and clinical outcomes; improved care planning; and improved health equity for tamariki Māori and other underserved children.