Healthcare Infrastructure Prioritization using Machine Learning - Abstract
The present effort presents a novel methodology for prioritizing healthcare infrastructure interventions to enhance resilience against climate-related hazards. Given the increasing frequency and severity of extreme weather events due to climate change, the study emphasizes the urgent need for effective prioritization strategies in healthcare systems, especially in low- and middle-income countries where resources are limited. The application of this methodology is illustrated through case studies in Colombia and Peru, highlighting regions with significant healthcare infrastructure at risk from flooding. The findings underscore the critical role of data-driven decision-making in enhancing healthcare resilience, ultimately aiming to safeguard health services and improve patient outcomes in the face of climate change.