Data Acquisition for Connected Network for EMSs Comprehensive Technical-support Using Artificial Intelligence

Currently, the domestic emergency medical system is disconnected from the information flow between hospitals, emergency sites, and control agencies, which are participants in the emergency medical system, and there are limitations in collecting and utilizing integrated data in emergency situations \[1\]. In addition, due to the lack of manpower for emergency services at the site and the lack of a real-time patient information delivery system, sufficient data records are not made to reflect the situation at the emergency site, and emergency patient information at the pre-hospital stage is not delivered to the transfer hospital \[1\]. Records of pre-hospital patient information that are currently being prepared are often written by hand, relying on the memory of paramedics after completing patient transfer, so the data is highly inaccurate and cannot be guaranteed to be reliable\[2\]. In particular, in the case of the four major serious emergency diseases, which are called cardiac arrest, severe trauma, cardiovascular emergency, and cerebrovascular emergency, the patient information identified in the emergency stage is very important in determining the severity, so it is very important to collect real-time patient information in the field to evaluate the severity, and based on the results of this evaluation, it is possible to select a medical institution suitable for treatment \[3,4\]. In addition, in the case of these serious emergency diseases, since targeted treatment is determined to be performed within a certain time, if the medical staff of the medical institution is aware of the patient's information before the patient arrives at the hospital, it is possible to prepare in advance for emergency treatment, thereby increasing the performance rate of emergency treatment within a reasonable time \[5,6,7\].