Neurosurgical care in Uganda faces significant challenges due to workforce shortages, infrastructure limitations, and a lack of digital health tools. Implementing electronic medical records (EMRs) and telehealth could improve neurosurgical outcomes, but adoption remains low. This systematic review examines barriers and facilitators to EMR and telehealth implementation in Ugandan neurosurgery to identify opportunities for improvement. A systematic review was conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines across PubMed, Google Scholar, and Scopus to identify studies published from 2007 to 2024 evaluating EMR and telehealth adoption in Uganda. Thematic analysis categorized barriers and facilitators into 10 distinct themes. Statistical analysis for frequency and correlation with technologies were performed using χ² analysis and Pearson residual tests, with statistical significance set at P < 0.1. Key barriers include technological infrastructure inefficiencies (24%), systemic and operational limitations (16%), and technical competency and training deficits (13%). Similarly, the primary facilitators include infrastructure reliability (22%), training and education investment (13%), and community engagement (13%). Notably, telehealth adoption was more significantly impacted by human resource limitations (P < 0.05), whereas EMR implementation was hindered by systemic inefficiencies (P < 0.05). Addressing Uganda's digital health gaps requires cost-effective and scalable strategies, including mobile-based EMRs, teleconsultation frameworks, and structured workforce training. Future research should focus on developing implementation models tailored to low-resource settings, ensuring feasibility and sustainability. This study aims to contribute to the growing discourse on digital solutions for resource-limited healthcare, with a particular emphasis on innovation in global neurosurgical care.

Healthcare access in Latin America is highly unequal, with rural and peri-urban populations disproportionately excluded from essential and specialized services. To address the persistent gaps often obscured by conventional urban–rural classifications, this study developed a machine learning framework integrating the Functional Urban Area (FUA) model with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Shannon entropy optimization to refine urbanization gradients and quantify inequities across 11 countries. High-resolution population density data from the Meta High Resolution Settlement Layer (HRSL, 2020) and CIESIN’s Gridded Population of the World (GPWv4, rev. 11), combined with healthcare facility locations from Healthsites.io, were processed in R to generate population-facility networks. Entropy optimization dynamically determined country-specific DBSCAN distance thresholds, ensuring representative clustering of functional urban and rural areas. Facilities were categorized by care level, and per-capita densities were compared across clusters. Results showed that entropy-optimized DBSCAN improved spatial precision over traditional approaches and revealed systemic urban bias: Peru, Chile, and Venezuela had the lowest hospital densities, while Ecuador, Bolivia, and Paraguay displayed the strongest rural deficits in primary care. Specialized services were overwhelmingly concentrated in urban clusters. This reproducible framework establishes a quantitative baseline for healthcare inequities, providing data-driven insights to inform the design of decentralized strategies to improve equitable access to care across Latin America.