Climate change is no longer just an environmental challenge. It’s a full-scale public health emergency unfolding in real time.
Disasters are escalating—wildfires, heat domes, plane emergencies, hurricanes and infrastructure failures. Behind each headline are real human consequences, including extreme heat driving ER visits, wildfire smoke worsening respiratory illness or floods cutting off access to care. Rising temperatures are expanding the reach of infectious diseases.
These impacts aren’t distant—they’re happening now and they’re hitting the most vulnerable communities the hardest.
I envision a world where those needs are addressed before the damage is done. The technology exists today. In fact, it’s already being deployed in countries outside the U.S.
Despite years of warning signs, though, too many American emergency and healthcare systems remain unprepared to handle even a single crisis—let alone multiple disasters in a year. The infrastructure they rely on was built for stability, not disruption. It’s slow, disconnected and reactive—designed for predictability in an age where unpredictability is the norm.
What’s needed isn’t incremental improvement. It’s a complete shift in how we operate. We need systems that sense, adapt and respond the moment conditions change.
The ability to act in real-time is no longer a competitive edge—it’s a public safety imperative. Whether it’s detecting a patient’s vitals deteriorating in a heatwave or rerouting ambulances during a flood, survival depends on speed.
We’re already deploying systems that meet this challenge. In Japan, we partnered with NTT Data to build D-Resilio—a disaster response platform that uses live data from drones, satellites and ground sensors to deliver dynamic evacuation routes. It blends real-time inputs with local protocols to protect lives during typhoons, floods, and landslides.
And that’s just one use case. With computer vision, edge intelligence and the ability to automate generative AI, we now have the tools to turn live data into real-time decisions and coordinated action. These systems transform live data into immediate decisions that protect lives. They respond as events unfold—helping healthcare workers, emergency responders and infrastructure operators act faster, smarter and with greater precision.
But here’s the concern – the U.S. should be leading this movement. Instead, we’re watching other nations move faster to adopt and scale these life-saving technologies. That’s beginning to change.
Recent international dialogues—including our visit to Saudi Arabia—signal a renewed urgency and a growing recognition that these systems must become core infrastructure at home, not just abroad.
At international events like WEF and HLTH, discussions are intensifying around the need to integrate AI into public health and emergency systems. But we can’t simply bolt AI onto legacy architecture and expect transformation. These systems must be reimagined—real-time, event-driven, distributed. That means moving from batch processing to live data streams. From rigid manual workflows to intelligent agents. From centralized cloud to adaptive edge.
This transformation is already underway. Healthcare providers, city planners, defense agencies and infrastructure leaders are beginning to adopt real-time intelligence as the foundation for climate resilience.
The next wave of innovation won’t be measured by how much data we collect—but by how fast we act on it. We either act in real time—or fall behind in real consequences.
