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

Year:
2022
Duration:
24 months
Approved budget:
$128,915.00
Researchers:
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:
Health Delivery 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.