Triage implementation in resource-limited emergency departments: sharing tools and experience from the Pacific region.

According to the World Health Organization's (WHO) Emergency Care Systems Framework, triage is an essential function of emergency departments (EDs). This practice innovation article describes four strategies that have been used to support implementation of the WHO-endorsed Interagency Integrated Triage Tool (IITT) in the Pacific region, namely needs assessment, digital learning, public communications and electronic data management.Using a case study from Vila Central Hospital in Vanuatu, a Pacific Small Island Developing State, we reflect on lessons learned from IITT implementation in a resource-limited ED. In particular, we describe the value of a bespoke needs assessment tool for documenting triage and patient flow requirements; the challenges and opportunities presented by digital learning; the benefits of locally designed, public-facing communications materials; and the feasibility and impact of a low-cost electronic data registry system.Our experience of using these tools in Vanuatu and across the Pacific region will be of interest to other resource-limited EDs seeking to improve their triage practice and performance. Although the resources and strategies presented in this article are focussed on the IITT, the principles are equally relevant to other triage systems.

Emergency department triage and COVID-19: Performance of the Interagency Integrated Triage Tool during a pandemic surge in Papua New Guinea.

OBJECTIVE: To determine the sensitivity of the Interagency Integrated Triage Tool to identify severe and critical illness among adult patients with COVID-19. METHODS: A retrospective observational study conducted at Port Moresby General Hospital ED during a three-month Delta surge. RESULTS: Among 387 eligible patients with COVID-19, 63 were diagnosed with severe or critical illness. Forty-seven were allocated a high acuity triage category, equating to a sensitivity of 74.6% (95% CI 62.1-84.7) and a negative predictive value of 92.7% (95% CI 88.4-95.8). CONCLUSION: In a resource-constrained context, the tool demonstrated reasonable sensitivity to detect severe and critical COVID-19, comparable with its reported performance for other urgent conditions.