Disaster medical teams are formed by hospitals in response to the manpower needs of a large number of injured and sick patients. The current planning of hospitals for a large number of disaster medical manpower is too superficial. The application of today's inspection methods in the treatment of a large number of injured patients is not as good as it is. Therefore, understanding the scene situation has become the key point of manpower deployment. Today's internet transmission speed and computer artificial intelligence technology are very different from 9 years ago. The investigators adopt one more simple and easy-to-operate inspection method and use artificial intelligence technology to assist.
Disaster medical teams are formed by hospitals in response to the manpower needs of a large number of injured and sick patients. However, the current planning of hospitals for a large number of disaster medical manpower is too superficial. The application of today's inspection methods in the treatment of a large number of injured patients is not as good as it is. Therefore, understanding the scene situation has become the key point of manpower deployment. Today's network transmission speed and computer artificial intelligence technology are very different from 9 years ago. The image transmission of the communication software to understand the scene will help the hospital's manpower deployment or should the investigators adopt a more easy-to-operate inspection method and use artificial intelligence technology to assist Implementation method: 82 patients with a large number of injuries in the Ali mountain traffic accident over the years were included, medical records were reviewed. The photos of patients recorded at the hospital site and existing vital signs of the patients were mainly used to make grading of the traditional STAR triage and the AIS to understand the possible differences in image interpretation and injury detection. Excluding patients with incomplete clinical data of treatment, the statistical methods of the samples were Correlation analysis and Wilcoxon signed rank test, and the p value of statistical significance was 0.05
Study Type
OBSERVATIONAL
Enrollment
90
Excluding patients with incomplete clinical data of treatment
St. Martin De Porres Hospital
Chiayi City, Taiwan
Abbreviated Injury Scale (AIS) Grading and Simple Triage and Rapid Treatment (START) Triage Method Combined with Film Transferring on Instant Message Improve Difference of Disaster Triage and Disaster Management
The AIS and START grading are usually defined in trauma disaster when patients are sent off to emergency room. The AIS is an anatomically-based injury severity scoring system that classifies each injury by body region on a 6 points scale from minor to maximal (currently untreatable) status. The START grading assign the disaster patients to one of the following four categories: grade 0: deceased/expectant (black), grade1: immediate (red), grade 2: delayed (yellow), grade 3: minor (green) The investigators use the recorded exterior photo, radiological film and vital sign data of the injured patients to re-define the AIS and START grading. The results are compared with the original data that are documented in our medical records and want to know that the consistency exist between two groups. Excluding patients with incomplete clinical data of treatment. Statistical methods were Correlation analysis and Wilcoxon signed rank test. The p value of statistical significance was 0.05
Time frame: 3 months
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