Cautiously optimistic: paediatric critical care nurses' perspectives on data-driven algorithms in low-resource settings-a human-centred design study in Malawi.

Background: Paediatric critical care nurses face challenges in promptly detecting patient deterioration and delivering high-quality care, especially in low-resource settings (LRS). Patient monitors equipped with data-driven algorithms that monitor and integrate clinical data can optimise scarce resources (e.g.

The role of healthcare providers and caregivers in monitoring critically ill children: a qualitative study in a tertiary hospital, southern Malawi.

Background: Critically ill children require close monitoring to facilitate timely interventions throughout their hospitalisation. In low- and middle-income countries with a high disease burden, scarce paediatric critical care resources complicates effective monitoring. This study describes the monitoring practices for critically ill children in a paediatric high-dependency unit (HDU) in Malawi and examines factors affecting this vital process.

Identifying critically ill children in Malawi: A modified qSOFA score for low-resource settings.

In low-resource settings, a reliable bedside score for timely identification of children at risk of dying, could help focus resources and improve survival. The rapid bedside Liverpool quick Sequential Organ Failure Assessment (LqSOFA) uses clinical parameters only and performed well in United Kingdom cohorts. A similarly quick clinical assessment-only score has however not yet been developed for paediatric populations in sub-Saharan Africa.