Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi: Development and Usability Study.

Background: Despite an increase in hospital-based deliveries, neonatal mortality remains high in low-resource settings. Due to limited laboratory diagnostics, there is significant reliance on clinical findings to inform diagnoses. Accurate, evidence-based identification and management of neonatal conditions could improve outcomes by standardizing care.

Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree.

Introduction: Improving peri- and postnatal facility-based care in low-resource settings (LRS) could save over 6000 babies' lives per day. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in LRS and are preventable through the implementation of cost-effective, simple, evidence-based interventions.

Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

Introduction: Every year 2.4 million deaths occur worldwide in babies younger than 28 days. Approximately 70% of these deaths occur in low-resource settings because of failure to implement evidence-based interventions.

Indirect impacts of the COVID-19 pandemic at two tertiary neonatal units in Zimbabwe and Malawi: an interrupted time series analysis.

Objectives: To examine indirect impacts of the COVID-19 pandemic on neonatal care in low-income and middle-income countries.

Design: Interrupted time series analysis.

Setting: Two tertiary neonatal units in Harare, Zimbabwe and Lilongwe, Malawi.

Usability-Focused Development and Usage of NeoTree-Beta, an App for Newborn Care in a Low-Resource Neonatal Unit, Malawi.

Background: Neonatal mortality is high in low-resource settings. NeoTree is a digital intervention for neonatal healthcare professionals (HCPs) aiming to achieve data-driven quality improvement and improved neonatal survival in low-resource hospitals. Optimising usability with end-users could help digital health interventions succeed beyond pilot stages in low-resource settings.

Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App and Dashboard: A 1-Year Digital Perinatal Outcome Audit.

Understanding the extent and cause of high neonatal deaths rates in Sub-Saharan Africa is a challenge, especially in the presence of poor-quality and inaccurate data. The NeoTree digital data capture and quality improvement system has been live at Kamuzu Central Hospital, Neonatal Unit, Malawi, since April 2019. To describe patterns of admissions and outcomes in babies admitted to a Malawian neonatal unit over a 1-year period a prototype data dashboard.

Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique.

Objectives: To determine whether a panel of neonatal experts could address evidence gaps in local and international neonatal guidelines by reaching a consensus on four clinical decision algorithms for a neonatal digital platform (NeoTree).

Design: Two-round, modified Delphi technique.

Setting And Participants: Participants were neonatal experts from high-income and low-income countries (LICs).

Electronic application to improve management of infections in low-income neonatal units: pilot implementation of the NeoTree beta app in a public sector hospital in Zimbabwe.

There are 2. 4 million annual neonatal deaths worldwide. Simple, evidence-based interventions such as temperature control could prevent approximately two-thirds of these deaths.

Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App: Digital Perinatal Outcome Audit.

Background: Mobile health (mHealth) is showing increasing potential to address health outcomes in underresourced settings as smartphone coverage increases. The NeoTree is an mHealth app codeveloped in Malawi to improve the quality of newborn care at the point of admission to neonatal units. When collecting vital demographic and clinical data, this interactive platform provides clinical decision support and training for the end users (health care professionals [HCPs]), according to evidence-based national and international guidelines.

The NeoTree application: developing an integrated mHealth solution to improve quality of newborn care and survival in a district hospital in Malawi.

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.

Evaluation of 'TRY': an algorithm for neonatal continuous positive airways pressure in low-income settings.

Background: Non-invasive respiratory support using bubble continuous positive airway pressure (bCPAP) is useful in treating babies with respiratory distress syndrome. Despite its proven clinical and cost-effectiveness, implementation is hampered by the inappropriate administration of bCPAP in low-resource settings. A clinical algorithm-'TRY' (based on Tone: good; Respiratory distress; Yes, heart rate above 100 beats/min)-has been developed to correctly identify which newborns would benefit most from bCPAP in a teaching hospital in Malawi.

Preschool children who are frequent attenders in emergency departments: an observational study of associated demographics and clinical characteristics.

Background: Unscheduled visits to emergency departments (ED) have increased in the UK in recent years. Children who are repeat attenders are relatively understudied.

Aims: To describe the sociodemographic and clinical characteristics of preschoolers who attend ED a large District General Hospital.