Predicting challenges and opportunities in improving snakebite envenoming management in Ghana.

BACKGROUND: Snakebite envenoming (SBE) is a neglected tropical disease and a major public health crisis in Africa. We integrated a maximum difference experimental design and machine learning (ML) models to predict challenges and opportunities for improving SBE management in Ghana. METHODS: This cross-sectional study included 137 healthcare workers, identified through multistage sampling, from August to December 2024 in two districts of the Eastern Region, Ghana. We employed five-fold cross-validation and analysed the dataset using 12 ML models. RESULTS: The most significant obstacle to managing SBE was 'delayed health-seeking behaviour by patients', followed by 'high cost of treatment' and 'inadequacy/shortage of antivenom'. On the opportunity side, 'increasing public awareness and knowledge about snakebite prevention and initial management before reaching a healthcare facility' was deemed crucial. Other opportunities such as 'conducting research into alternative management for snakebite to complement or replace antivenoms' and 'increasing awareness and knowledge about snakebite management among healthcare workers' were also recognized as trade-offs. CONCLUSIONS: This study significantly contributes to the existing research on artificial intelligence/ML, as previous studies have not quantified the challenges and opportunities in managing SBE. Policymakers and healthcare providers can use these findings to implement strategies such as promoting better health-seeking behaviours, subsidizing treatment costs, ensuring access to effective antivenom and raising public awareness about snakebite prevention and initial care.