AI Integrated Emergency Call Analysis and Dispatch Workflow

Automated Emergency Call Analysis and Dispatch

1. Emergency Call Receipt

1.1 Call Reception

Emergency calls are received by the dispatch center through various channels (phone, mobile app, etc.).

1.2 Initial Call Screening

AI-driven tools such as Natural Language Processing (NLP) algorithms are employed to analyze the caller’s speech in real-time, identifying keywords and urgency levels.

2. Call Analysis

2.1 AI-Enhanced Triage

Utilize AI algorithms like IBM Watson or Google Cloud AI to assess the medical situation based on the caller’s input, categorizing emergencies into critical, urgent, or non-urgent.

2.2 Risk Assessment

Implement AI tools such as Predictive Analytics to evaluate potential risks and outcomes based on historical data, assisting in prioritizing the dispatch.

3. Dispatch Coordination

3.1 Automated Resource Allocation

Leverage AI systems like CAD (Computer-Aided Dispatch) software to automatically assign the nearest available emergency response units based on real-time location data.

3.2 Dynamic Routing

Integrate AI-driven mapping tools (e.g., Waze for Cities) to provide optimized routing for emergency vehicles, taking traffic conditions into account.

4. Real-Time Monitoring and Updates

4.1 Continuous Data Analysis

Utilize AI platforms to monitor the status of dispatched units and the evolving situation at the emergency scene, enabling timely updates to responders.

4.2 Communication Enhancement

Implement AI chatbots and voice assistants to facilitate communication between dispatchers and responders, ensuring clear and efficient information transfer.

5. Post-Response Evaluation

5.1 Data Collection

Gather data from the incident response, including timestamps, resource allocation, and outcomes, for analysis.

5.2 AI-Driven Feedback Loop

Utilize machine learning algorithms to analyze the effectiveness of the response, identifying patterns and areas for improvement in future dispatches.

6. Reporting and Compliance

6.1 Automated Reporting

Generate automated reports using AI tools that compile incident data, response times, and resource utilization for compliance and performance evaluation.

6.2 Quality Assurance

Employ AI systems to audit responses against established protocols, ensuring adherence to standards and identifying training needs for personnel.