More than two-thirds of newborn lives could be saved worldwide if evidence-based interventions were successfully implemented. We developed the NeoTree application to improve quality of newborn care in resource-poor countries. The NeoTree is a fully integrated digital health intervention that combines immediate data capture, entered by healthcare workers (HCW) on admission, while simultaneously providing them with evidence-based clinical decision support and newborn care education. We conducted a mixed-methods intervention development study, codeveloping and testing the NeoTree prototype with HCWs in a district hospital in Malawi. Focus groups explored the acceptability and feasibility of digital health solutions before and after implementation of the NeoTree in the clinical setting. One-to-one theoretical usability workshops and a 1-month clinical usability study informed iterative changes, gathered process and clinical data, System Usability Scale (SUS) and perceived improvements in quality of care. HCWs perceived the NeoTree to be acceptable and feasible. Mean SUS before and after the clinical usability study were high at 80.4 and 86.1, respectively (above average is >68). HCWs reported high-perceived improvements in quality of newborn care after using the NeoTree on the ward. They described improved confidence in clinical decision-making, clinical skills, critical thinking and standardisation of care. Identified factors for successful implementation included a technical support worker. Coproduction, mixed-methods approaches and user-focused iterative development were key to the development of the NeoTree prototype, which was shown to be an agile, acceptable, feasible and highly usable tool with the potential to improve the quality of newborn care in resource-poor settings.
Keywords: data collection; health facilities; infant mortality; mHealth; quality of healthcare; user-computer interface.