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

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-ofthe-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.Msandeni Esther Chiume and Simbarashe Chimhuya are joint last authors.

Persistent barriers to achieving quality neonatal care in low-resource settings: perspectives from a unique panel of frontline neonatal health experts

“…We conducted a short qualitative study with a group of neonatal experts who had all previously participated in a Delphi study with the research team (ME, CC, FF, MH) to refine a series of clinical diagnostic algorithms for a digital health intervention for neonatal care in LRS (Neotree). [14][15][16] Twenty-two neonatal experts were invited to participate via email. Non-responders were sent a reminder email or sent a WhatsApp message two weeks later.…”

Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model

“…The Neotree is an open-source digital health system for newborn care in LRS. [16, 17] It combines evidence-based clinical decision support, education and digital data capture at admission and discharge. It is embedded in routine practice at three neonatal units in sub-Saharan Africa (Kamuzu Central Hospital, Malawi; SMCH, Zimbabwe; and Chinhoyi Provincial Hospital, Zimbabwe).…”

Effects of the COVID-19 pandemic on the outcomes of HIV-exposed neonates: a Zimbabwean tertiary hospital experience

“…An estimated 12,000 neonates are delivered annually and the neonatal unit often functions at 140% of its 100 cot-bed capacity. The results of the development and pilot implementation of Neotree are reported elsewhere [10][11][12]. Previous analysis of Neotree data (from 1 st June 2019 to 25 th September 2020) showed admissions to the NNU at Sally Mugabe Central Hospital were not directly impacted during the initial months of the COVID-19 pandemic, but dropped substantially during a nurses' strike in response to challenges exacerbated by the pandemic [13].…”