Effects of self-management interventions on self-care ability and quality of life in patients with urinary ostomy after bladder cancer: a systematic review and meta-analysis.

Neonatal mortality remains a major contributor to under-five mortality globally, with the highest burden concentrated in low- and middle-income countries (LMICs) (–). Despite improvements in maternal and newborn care, many neonatal deaths remain preventable through earlier identification of clinical deterioration and timely intervention (). In resource-constrained neonatal settings, delayed recognition of high-risk neonates is frequently compounded by staffing shortages, inconsistent documentation practices, limited monitoring capacity, and fragmented clinical workflows. Machine learning (ML)-driven neonatal risk stratification tools have increasingly been explored to support early risk identification and clinical decision-making (, ). These tools have the potential to strengthen the prioritization of care, improve recognition of vulnerable neonates, and support more structured clinical assessment in high-burden environments. However, while many predictive models demonstrate acceptable technical performance during pilot implementation, translation into routine clinical practice remains challenging (, ). The implementation gap between pilot innovation and sustainable institutional adoption is particularly pronounced in LMIC health systems, where governance complexity, workforce shortages, infrastructure instability, and financing constraints may impede long-term integration even when frontline usability is favorable (, ). Previous digital health research has shown that technologies insufficiently aligned with organizational workflows, documentation systems, and institutional governance structures may introduce operational strain rather than implementation efficiency (–).