arXiv:2605.23378v1 Announce Type: cross Abstract: Ambulance response is time-critical in out-of-hospital cardiac arrest (OHCA), where dispatchers must balance timely arrivals with limited fleet capacity. Static territories and deterministic travel-time estimates are vulnerable to dynamic congestion, while always-dual dispatch adds redundancy but consumes fleet capacity. We propose IDEAL (Intelligent Dual dispatch of Emergency AmbuLances), a selective dual-dispatch framework that sends a second ambulance only when the optimistic gap between primary and secondary paths exceeds a threshold. IDEAL
The paper leverages recent advancements in contextual travel-time predictions and computational optimization to address a critical real-world problem with AI agents. This comes as AI applications in public services are gaining traction.
This AI-driven framework optimizes time-critical emergency response, potentially improving patient outcomes and resource allocation under dynamic conditions. It represents a concrete application of AI agents in public infrastructure.
Emergency dispatch systems can move from static, deterministic models to dynamic, AI-informed decision-making, selectively deploying resources based on real-time conditions rather than fixed protocols. This changes how emergency services are coordinated.
- · Emergency medical services
- · Smart city technology providers
- · AI/Optimization software developers
- · Public health outcomes
- · Traditional static dispatch systems
- · Urban populations without access to advanced infrastructure
- · Legacy EMS software vendors
Improved efficiency in ambulance dispatch and potentially higher survival rates for time-sensitive medical emergencies.
Increased trust and investment in AI applications for critical public services, leading to broader adoption across other sectors like fire or police.
The proliferation of such systems could generate vast amounts of real-time urban data, enabling more comprehensive smart city planning and infrastructure optimization.
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Read at arXiv cs.LG