Senate Advances AI Integration in National Emergency Medical System

The Senate’s emergency medicine reform working group said it had examined ways to apply artificial intelligence to improve crisis and emergency medical management, aiming for faster, more accurate assistance to the public and more equitable access to services. They stressed that in emergencies time can be a matter of life or death, while the current system struggles with fragmented data, growing workloads for responders, increasingly complex crises, and unequal service availability across regions. Dr. Prem Sak outlined four main goals for AI use: speeding up response, improving assessment and decision‑making accuracy, enhancing safety for patients and staff, and expanding service coverage to both urban and rural areas under a clear supervisory framework. The proposed architecture covers the six‑layer structure of the National Institute of Emergency Medicine, linking citizens, patients, responders, doctors and incident commanders with emergency infrastructure such as ambulances, command centers, radio and telephone systems, automated external defibrillators, CCTV, sensors, drones and hospitals. This feeds into a digital emergency‑medicine platform — including the NDEMS 1669 hotline, telemedicine, dispatch and payment systems — before consolidating into a national data centre that captures incident, patient, resource, hospital, geographic and treatment‑outcome information. AI would analyse and forward data to the crisis and emergency medical command centre, with every layer operating under governance, cyber‑security, personal‑data protection and AI‑safety rules. The meeting listed ten steps where AI could be used throughout the life‑saving process, from risk forecasting to prevent incidents, opening multiple access channels to the 1669 hotline via phone, app, SMS, video call and LINE with automatic location tagging, to transcribing calls, extracting key points and summarising situations to support responders. Additional AI functions include assessing patient urgency, dispatching the nearest and most appropriate rescue unit while calculating routes in real‑time traffic, developing a field digital assistant to guide procedure checklists, verify medication and equipment, and capture documentation by voice to ease workload. Real‑time vital‑sign feeds from ambulances would enable tele‑consultations and AI‑directed hospital selection based on condition severity, hospital capability and bed, operating‑theatre and emergency‑room readiness. The plan also covers entitlement verification, document and disease‑code summarisation to support compensation claims, and analysis of treatment outcomes and service quality to feed continuous system improvement.