EDFlow AI: Real-Time Multi-Agent Coordination for Emergency Departments

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.

What We Built

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:

• Analyzes the patient’s condition using Claude Sonnet 4 and activates the appropriate protocol (STEMI, Stroke, or Trauma)
• Reserves the correct ICU bed with protocol-specific equipment
• Orders time-critical medications and stages them at bedside
• Requests STAT laboratory tests with critical priority
• Pages specialist physicians and activates procedure rooms
• Assigns nursing staff and emergency equipment
• Sends instant WhatsApp alerts to the entire medical team

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.

How It Works

We engineered EDFlow AI around seven specialized agents using Fetch.ai’s uAgents, ASI:One, and Agentverse:

1. ED Coordinator Agent — The orchestrator powered by Claude Sonnet 4. Receives ambulance reports via ASI:One, analyzes patient vitals and symptoms, detects emergency protocols, and broadcasts coordination requests to all specialized agents simultaneously.
2. Resource Manager — Monitors ED capacity, allocates trauma bays, assigns nursing staff, and stages emergency equipment.
3. Bed Management — Queries and reserves available beds by type (Cardiac ICU, Neuro ICU, Trauma Bay) with appropriate equipment.
4. Lab Service — Orders protocol-specific STAT tests automatically (Troponin for STEMI, CT Brain for Stroke, Type & Cross for Trauma).
5. Pharmacy Agent — Prepares time-critical medications (Aspirin/Heparin for STEMI, tPA for Stroke, Tranexamic Acid for Trauma) and stages them at bedside.
6. Specialist Coordinator — Pages on-call specialists, activates procedure rooms (Cath Lab, CT Suite, OR), and mobilizes support teams.
7. WhatsApp Notification Agent — Sends real-time alerts to medical staff phones via Twilio WhatsApp API with patient details, activated protocol, and assigned resources.

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 Interface

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 in Action

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.

What’s Next

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:

• Paramedics dictating patient vitals while driving
• Nurses activating code blue protocols instantly
• Doctors requesting labs while performing CPR
• Quick voice queries: “EDFlow, ICU bed availability?”

Additional roadmap:

• Expanded protocol coverage: Sepsis, Cardiac Arrest, Massive Transfusion
• Multi-language support and predictive ED capacity forecasting
• EHR integration (Epic, Cerner) for automatic patient history
• Clinical validation through multi-center trials and FDA clearance

The Numbers That Matter

• ⚡ <10 seconds — Consistent coordination time across all protocols
• 🎯 95%+ reliability — Agent response rate with fault tolerance
• ✅ Zero errors — Automated workflow eliminates miscommunication
• 📱 98%+ delivery — WhatsApp notifications to medical staff

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.

The Team

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.