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In emergency medicine, time is tissue. Every minute of delay during a heart attack destroys 1.2 million cardiac cells permanently. Yet the biggest bottleneck in emergency departments isn’t medical expertise or equipment, it’s coordination logistics. When an ambulance reports a STEMI patient, nurses spend 3+ minutes making sequential phone calls: lab, pharmacy, cardiology, ICU beds, equipment staging. The medical team stands ready, but can’t begin treatment.
EDFlow AI eliminates this delay. We built a multi-agent autonomous system that replaces sequential coordination with parallel AI-driven workflows, reducing emergency preparation time from 180+ seconds to under 10 seconds.
EDFlow AI is a real-time emergency department coordination platform powered by seven autonomous AI agents. When an ambulance radios ahead about a critical patient, our system simultaneously:
The result? What traditionally takes 3–5 minutes happens in under 10 seconds — saving approximately 3.6 million cardiac cells for a single STEMI patient.
We engineered EDFlow AI around seven specialized agents using Fetch.ai’s uAgents, ASI:One, and Agentverse:
All agents read from and write to a live hospital database (JSONBin), with optimistic locking to prevent race conditions. The system includes 10-second timeout mechanisms, automatic retries, and graceful degradation for 95%+ reliability.
The ambulance-facing interface uses ASI:One’s chat platform. Paramedics simply type patient vitals and symptoms in natural language — no special formatting required. Within 10 seconds, they receive a comprehensive preparation report showing bed assignments, medication status, lab orders, specialist alerts, and resource allocation. The ED Coordinator aggregates all agent responses into a single, actionable summary.
Fetch.ai’s agent ecosystem was essential for building a production-ready system in 36 hours. With uAgents, we created seven autonomous agents that communicate directly without central bottlenecks. Agentverse provided serverless hosting with automatic scaling for multiple concurrent emergencies. ASI:One gave us a ready-made chat interface requiring zero frontend development. The Almanac Contract enabled agent discovery and verified identity, while standardized chat protocols ensured reliable communication across all agents.
In traditional microservices, we’d still be writing integration code. With Fetch.ai, we deployed a working system in a weekend.
We’re building a voice-activated emergency coordination system integrated directly into EDFlow AI. Nurses and ambulance crews will activate protocols hands-free using voice commands — no typing, no screens, no delays.
Beyond that, we’re designing a custom wearable device that nurses can clip to badges or wear on wrists. Activated by wake-word or button press, the device enables:
Additional roadmap:
For a single STEMI patient, 3 minutes saved = 3.6 million cardiac cells preserved. For a hospital with 200 STEMI cases annually, that’s 720 million cardiac cells saved per year. Multiply across stroke and trauma protocols — the potential impact is enormous.
Yugm Patel — linkedin.com/in/yugmpatel
Pruthvik Sheth — linkedin.com/in/pruthviksheth
Shubham Kothiya — linkedin.com/in/shubhamkothiya
Rutuja Kadam — linkedin.com/in/rutujakadam
EDFlow AI proves that Fetch.ai agents can deliver under real-time, high-consequence conditions where every second counts. We’re continuing to refine and expand this technology — if emergency medicine and AI excite you, we’d love to connect and explore what’s next.
Built with ❤️ by Team EDFlow AI using Fetch.ai uAgents & Claude AI
EDFlow AI: Real-Time Multi-Agent Coordination for Emergency Departments was originally published in Fetch.ai on Medium, where people are continuing the conversation by highlighting and responding to this story.
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