Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree. Issue 1 (6th April 2022)

• Record Type: Journal Article
• Authors: Heys, Michelle; Kesler, Erin; Sassoon, Yali; Wilson, Emma; Fitzgerald, Felicity; Gannon, Hannah; Hull‐Bailey, Tim; Chimhini, Gwendoline; Khan, Nushrat; Cortina‐Borja, Mario; Nkhoma, Deliwe; Chiyaka, Tarisai; Stevenson, Alex; Crehan, Caroline; Chiume, Msandeni Esther; Chimhuya, Simbarashe
• Is Part Of: Learning health systems. Volume 7:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-06
• Subjects: behavioural sciences -- global health -- health services -- neonatal; Medical care -- Research -- Periodicals; Medical informatics -- Periodicals; Health planning -- Periodicals; 362.1068
• DOI: 10.1002/lrh2.10310
• Languages: English
• ISSNs: 2379-6146

Abstract

Abstract: 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. However, their implementation is challenging in healthcare systems where one in four babies admitted to neonatal units die. In high‐resource settings healthcare systems strengthening is increasingly delivered via learning healthcare systems to optimise care quality, but this approach is rare in LRS. Methods: Since 2014 we have worked in Bangladesh, Malawi, Zimbabwe, and the UK to co‐develop and pilot the Neotree system: an android application with accompanying data visualisation, linkage, and export. Its low‐cost hardware and state‐of‐the‐art software are used to support healthcare professionals to improve postnatal care at the bedside and to provide insights into population health trends. Here we summarise the formative conceptualisation, development, and preliminary implementation experience of the Neotree. Results: Data thus far from ~18 000 babies, 400 healthcare professionals in four hospitals (two in Zimbabwe, two in Malawi) show high acceptability, feasibility, usability, and improvements in healthcare professionals' ability to deliver newborn care. The data also highlight gaps in knowledge in newborn care and quality improvement. Implementation has been resilient and informative during external crises, for example, coronavirus disease 2019 (COVID‐19) pandemic. We have demonstrated evidence of improvements in clinical care and use of data for Quality Improvement (QI) projects. Conclusion: Human‐centred digital development of a QI system for newborn care has demonstrated the potential of a sustainable learning healthcare system to improve newborn care and outcomes in LRS. Pilot implementation evaluation is ongoing in three of the four aforementioned hospitals (two in Zimbabwe and one in Malawi) and a larger scale clinical cost effectiveness trial is planned.