Impact and cost-effectiveness of Neotree, a digital data capture and decision support tool designed to improve neonatal survival in Zimbabwe: an interrupted time series analysis and economic evaluation.

INTRODUCTION: Many neonatal deaths are avoidable using existing low-cost evidence-based interventions. This study evaluated the effectiveness and cost-effectiveness of Neotree, a digital quality improvement tool combining data capture with education and clinical decision support, implemented in a Zimbabwean hospital. METHODS: Neotree was implemented in Chinhoyi Provincial Hospital (CPH) in December 2020. Using data collected for all neonates admitted to CPH from March 2020 to October 2023, a single group interrupted time series analysis was conducted to estimate the impact of Neotree implementation. Subgroup analyses explored the impact in low birth weight (1.5-2.5 kg) neonates, a key group targeted by the intervention.Activity-based costing and expenditure approaches estimated costs of developing and implementing Neotree in CPH from a provider perspective. Both total within-study costs and total costs at scale were estimated and used to derive cost per life saved, cost per life year saved and cost per healthy life year (HLY) gained. RESULTS: Analysis suggests reduced overall mortality in the post-implementation period, though this difference was not statistically significant (RR: 0.877, 95% CI 0.541 to 1.423, p=0.596). This was primarily driven by reduced mortality among the low birth weight subgroup (RR: 0.356, 95% CI 0.127 to 1.002, p=0.051). Cost-effectiveness analysis based on an assumed mortality impact in this subgroup suggests a within-study cost of around $28.44 per HLY gained, reducing to $6.35 per HLY gained at scale, substantially below the range of potential cost-effectiveness thresholds considered for Zimbabwe (US $17- US $855). CONCLUSION: Neotree is a potentially low-cost and highly cost-effective digital quality improvement tool to improve newborn care, morbidity and survival, while also providing quality data. This study contributes to limited economic evidence of mHealth tools in low-income and middle-income settings.