AI Integration in Emergency Services Triage and Dispatch Workflow

Introduction

This workflow outlines the integration of AI technologies in emergency services triage and dispatch processes, enhancing efficiency and effectiveness in responding to emergencies.

Initial Call Intake

1. Call is received by the 911 dispatch center.
2. An AI-powered voice analysis system is activated to:
   • Detect caller stress levels.
   • Identify background noises and sounds.
   • Flag potential high-priority emergencies.
3. A natural language processing (NLP) chatbot engages the caller to gather initial information:
   • Location of the emergency.
   • Nature of the emergency.
   • Number of people involved.
   • Presence of weapons or other hazards.
4. The AI system automatically categorizes the call type and urgency level based on the gathered data.

Triage Assessment

1. The AI triage system analyzes call details and recommends a priority level.
2. The human dispatcher reviews the AI recommendation and confirms or adjusts the priority.
3. An AI decision support tool provides suggested questions to gather additional details based on the emergency type.
4. The dispatcher asks follow-up questions guided by AI suggestions.
5. The machine learning system continuously updates the triage model based on outcomes.

Resource Allocation

1. The AI-powered resource management system assesses:
   • Available units and personnel.
   • Unit locations and estimated travel times.
   • Specialized equipment needs.
2. The system recommends the optimal unit(s) to dispatch.
3. The dispatcher confirms or modifies the dispatch recommendation.
4. An automated notification is sent to selected units with incident details.

Ongoing Incident Management

1. The AI system monitors radio communications and on-scene updates.
2. Natural language processing extracts key details from radio chatter.
3. Incident details are automatically updated in the dispatch system.
4. The AI analyzes the developing situation and recommends additional resources if needed.
5. Predictive analytics forecast potential escalation scenarios.

Post-Incident Analysis

1. The AI system compiles incident data, response times, and outcomes.
2. Machine learning algorithms identify trends and areas for improvement.
3. Automated reports are generated for review by management.

This AI-enhanced workflow improves emergency services triage and dispatch in several ways:

• Faster initial assessment: AI voice analysis and NLP chatbots quickly gather critical information.
• More consistent triage: Machine learning algorithms ensure standardized prioritization.
• Optimized resource allocation: AI systems can rapidly evaluate multiple factors to recommend the best units.
• Improved situational awareness: NLP and predictive analytics keep dispatchers better informed.
• Continuous improvement: Machine learning allows the system to become more accurate over time.

By integrating these AI-driven tools, emergency services can respond more efficiently and effectively to calls, potentially saving lives and improving outcomes.