Data Analytics for DRM in Health Systems: AI-Driven Tools for Cross-Sectoral Resilience
In this session, we explore how combining risk and socio-economic data can help to enhance the disaster resilience of health systems. We will present analytical tools integrating data on hazards that affect people and health services with health indicators and socio-economic data followed by an interactive discussion of real-world applications, informing strategic and resilient cross-sectoral infrastructure investments. We explore how combining risk and socio-economic data can help to enhance the disaster resilience of health systems. Shocks affect peoples’ health, livelihood, and well-being. They have a significant impact on health services and patient care delivery through structural damage to buildings; interruption of care; loss of lighting, heating, and cooling; extensive contamination of building structures, equipment, and supplies with microorganisms; and fire hazards due to erosion of electrical systems or equipment to name a few. Disaster risk exposures for climatological hazards are expected to increase with climate change and in some cases through human development e.g., land use changes and the built environment. These challenges are inherently interconnected, creating a landscape where various risk information overlap across many sectors within and beyond health. Data driven, cross-sectoral, multi-hazard analyses are an essential tool for policy makers to effectively manage these evolving risks. This session explores how risk and vulnerability assessments empower sectoral stakeholders to attain a more comprehensive perspective, thereby providing the necessary insights for prioritizing investments that can be adapted to the ever-evolving landscape of such an intricate environment and have beyond sector resilient impacts.
Organized by: GFDRR thatic area Climate and Disaster Risk Management for Health Systems, WHO
