The Neotree combines digital data capture, export and visualisation, education and clinical decision and management support with quality improvement, community involvement and engagement and open code and data to create a learning healthcare system for newborn care in low resource settings.
Around 70% of newborn deaths globally could be prevented by implementing low-cost, evidence-based interventions effectively. However, it is not clear how to do this, especially in low resource settings where up to 1 in 4 babies admitted to newborn care units die. There also remains– almost universally in low resource settings– a lack of reliable data sources and health information systems for counting births, stillbirths, newborn deaths and characterising the causes of death.
Over the last seven years we have been developing the Neotree with healthcare professionals to capture accurate and timely clinical data at the bedside. In the process, we produce robust, context-specific evidence of what works in improving newborn survival. We are also testing, evaluating, and refining the acceptability, feasibility and usability of the system in three hospitals–two in Zimbabwe and one in Malawi– in addition to collecting quality improvement data, WHO quality of care indicators and cost data.
The Neotree system collects comprehensive clinical and demographic data on newborns after birth as part of usual clinical workflow on admission and discharge to neonatal units. These data are then exported, linked to laboratory data and visualised on data dashboards for neonatal teams and healthcare planners. It has the ability to link to individual national health care records (EHR) and aggregate health records (e.g., DHISv2).
Evidence-based guidelines are converted into digital algorithms, embedded within the app digitalised and delivered at the bedside to provide immediate, personalised and context specific provide clinical diagnostic and management support. Education messages are provided in response to data entry and with management guidelines embedded to guide and reinforce training in newborn care. User-centric iterative development has been used with state-of-the art software, low-cost hardware, open-source code and local data ownership.
UCL co-creates app that will improve newborn care globally