HEALTH IN CLIMATE AI HACKATHON - HEALTHINEERS

Problem:
Extreme heatwaves, wildfires, and deteriorating air quality are growing in frequency and severity, overwhelming local resources and healthcare systems. Current disaster response is largely reactive—NGOs and Emergency Operations Centers (EOCs) often lack predictive analytics and optimized allocation tools to plan ahead. As a result, resources are misallocated, emergency departments are overburdened, and vulnerable populations (elderly, chronically ill, and low-income residents) remain under-warned and over-exposed to hazards.

Primary Users:

NGOs
County Emergency Managers
Healthcare Systems
At-Risk Individuals

Data Sources:
Milliman dataset with environmental data, geospatial mapping, healthcare utilization, and demographics.

Model / Approach

The platform combines predictive analytics and AI-driven optimization to transform emergency planning:

Forecasting: Time-series models predict heatwaves and air-quality spikes while regression models estimate ED surges and resource needs.
Resource Optimization: Multi-objective linear programming balances limited resources against population needs and risk levels, using disaster tags to align resources with specific hazards.
AI-Driven Alerts: Automated recommendations generate draft alerts for NGOs and EOCs, while personalized SMS/WhatsApp warnings reach vulnerable individuals using de-identified health attributes.
Visualization: Interactive maps, detail cards, and dashboards provide situational awareness and system-wide summaries for decision-makers.

Deployment:
Deploy on Vercel for hosting and delivery.

Impact Metrics: