Cautiously optimistic about data-driven algorithms in paediatric critical care, nurses’ perspectives in low-resource settings: a pre-implementation and human-centred design study in Malawi

Abstract

Introduction 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 integrate monitor and clinical data can optimise scarce resources (e.g. trained staff) offering solutions to these challenges. Poor algorithm output design and workflow integration are important factors hindering successful implementation. This study aims to explore nurses' perspectives to inform the development of a data-driven algorithm and user-friendly interface for future integration into a continuous monitoring system for critical care in LRS. Methods Human-centred design methods, including contextual inquiry, semi-structured interviews, and co-design sessions, were carried out at the high-dependency units of Queen Elizabeth Central Hospital and Zomba Central Hospital in Malawi. Triangulating these methods, and employing qualitative content analysis principles, we identified what algorithm could assist nurses and used co-creation methods to design a user interface prototype. Results Workflow observations demonstrated the effects of personnel shortages and limited monitor equipment availability for vital sign monitoring. Interviews emphasised the advantages of predictive algorithms in anticipating deterioration, underlining the need to integrate the algorithm’s output, the (constant) monitoring data, and the patient's present clinical condition. Nurses preferred a scoring system represented with familiar scales and colour codes. During co-design sessions, trust, usability and context specificity were emphasized as requirements for these algorithms. Four prototype components were examined, with nurses favouring explainable and transparent scores represented by colour codes and visual representations of score changes. Conclusions Nurses in LRS perceive that data-driven algorithms, especially for predicting patient deterioration, could improve the provision of critical care. This can be achieved by translating nurses' perspectives into design strategies, as has been carried out in this study. The lessons learned are summarised as actionable pre-implementation recommendations for the development and implementation of data-driven algorithms in LRS.