NCT05127070

Evaluating the NeoTree in Malawi and Zimbabwe

Evaluating the NeoTree: An eHealth Solution to Reduce Neonatal Mortality in Two Low Income Countries: Malawi and Zimbabwe

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 19,000 (estimated)
• Sponsor: University College, London · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: —

Summary

Neonatal mortality remains unacceptably high. Globally, the majority of mothers now deliver in health facilities in low resource settings where quality of newborn care is poor. Health systems strengthening through digitial quality improvement systems, such as the Neotree, are a potential solution. The overarching aim of this study is to complete the co-development of NeoTree-gamma with key functionalities configured, operationalised, tested and ready for large scale roll out across low resource settings. Specific study objectives are as follows: 1. To further develop and test the NeoTree at tertiary facilities in Malawi and Zimbabwe 2. To investigate HCPs and parent/carer view of the NeoTree, including how acceptable and usable HCWs find the app, and potential barriers and enablers to implementing/using it in practice. 3. To collect outcome data for newborns from representative sites where NeoTree is not implemented. 4. To test the clinical validity of key NeoTree diagnostic algorithms, e.g. neonatal sepsis and hypoxic ischaemic encephalopathy (HIE) against gold standard or best available standard diagnoses. 5. To add dashboards and data linkage to the functionality of the NeoTree 6. To develop and test proof of concept for communicating daily electronic medical records (EMR) using NeoTree 7. To initiate a multi-country network of newborn health care workers, policy makers and academics. 8. To estimate cost of implementing NeoTree at all sites and potential costs at scale

Detailed description

Every year 2.4 million newborn deaths occur worldwide. Up to 70% of newborn deaths are avoidable with implementation of standard-technology, evidence-based interventions. Health systems strengthening and education and training in newborn care are key to saving newborn lives. Implementation of evidence based interventions and guidelines can be supported through provision of reliable data systems, clinical decision support tools and education. Using open-source code and maintaining local data ownership the investigators have used iterative, human- and user-centered design methods and agile processes in software and data management development and design to develop the Neotree: a digital quality improvement system for postnatal facility-based care in low resource settings. The Neotree aims to improve quality of care and newborn survival through combining data-capture, clinical decision-support, education in newborn care, and feedback of data to dashboards and national aggregate data systems. The investigators found the concept of device-enabled decision support to improve newborn care to be acceptable during workshops with healthcare professionals in Bangladesh (n\~15; 2014) and developed and delivered a prototype of the app. Following this, the investigators co-developed and piloted an early version of the NeoTree with Malawian Healthcare Professionals (HCPs) (n=46; 2016-2017), who reported it was easy to use and helped them deliver quality care. The research project described in this protocol will enable the investigators to complete the co-development of the Neotree in Zimbabwe and Malawi and generate evidence for how to test it at scale. Methods and analysis: Mixed methods (i) intervention co-development and optimisation, (ii) pilot implementation evaluation and (iii) economic evaluation study. The Neotree will be implemented in two hospitals in Zimbabwe, and one in Malawi. Clinical and demographic newborn data will be collected via the Neotree, in addition to behavioural science informed qualitative and quantitative implementation evaluation data, cost data, measures of quality newborn care and usability data over the 2-year study period. Six-months of newborn outcome data and cost data will be collected from 2 hospitals receiving usual care for comparison. Case-fatality rate data will inform sample size calculations and study design for a large scale roll out. Training manuals will be refined. Neotree clinical decision support algorithms will be optimised according to best available evidence and clinical validation studies. Our overall vision is to use best practice and information technology to improve clinical decisions for newborn care and increase rates of newborn survival in under-resourced health care settings. In this study, the care for an estimated 15,000 babies across the three test sites will be impacted by the Neotree. Through successful rollout across Zimbabwe and Malawi - the care for nearly 300,000 babies could be improved annually.

Conditions

• Prematurity
• Neonatal Encephalopathy
• Neonatal Sepsis
• Neonatal Death
• Neonatal Seizure
• Neonatal Jaundice
• Neonatal Hypoglycemia
• Neonatal Hypothermia
• Neonatal Disorder
• Neonatal Respiratory Failure

Interventions

Type	Name	Description
DEVICE	Neotree	The Neotree is a digital app, data collection and quality improvement system. collecting and collating routine health data at the bedside for babies on admission and discharge and for laboratory results. it provides point of care education and clinical decision support to optimise the clinical care of sick and vulnerable newborns according to approved and best available clinical guidelines.

Timeline

• Start date: 2019-10-01
• Primary completion: 2022-09-01
• Completion: 2022-09-01
• First posted: 2021-11-19
• Last updated: 2022-05-18

Locations

5 sites across 2 countries: Malawi, Zimbabwe