ObjectiveTo discuss the influence of tutorial system in standardized emergency residency training. MethodWe reviewed the settings and management of tutorial system in the Emergency Department of West China Hospital since 2009, and summarized the achievements. ResultsThrough practice in these years, the clinical skills, teaching abilities and scientific research capability of standardized-training emergency residents were enhanced greatly. ConclusionsTutorial system facilitates standardized emergency residency training.
ObjectiveTo investigate the status quo of emergency resources in all township hospitals in a county of Aba Autonomous Region. MethodWe set up a uniform electronic version questionnaire between April 15th and 18th, 2015. The leaders of township hospitals filled in their information and uploaded the data including emergency medical services, human resources, medical device and technology application situation. Then, the data were statistically analyzed. ResultsFor these township hospitals, the service population was 2 206.05±846.95, the service radius was (25.5±14.3) km. The number of registered doctors per 1 000 people of resident population was 1.52, the number of registered nurses per 1 000 people of resident population was 0.47, and the number of hospital beds per 1 000 people of resident population was 1.69. The staff in all township hospitals included 74 doctors and 23 nurses. The constitution of positional titles and academic qualifications of doctors and nurses in these township hospitals was not significantly different (P>0.05). All township hospitals had a total of six ambulances, one of which was ambulance for rescue and monitoring, and the others were ordinary ambulances. The devices equipped in the ambulances and hospitals were not sufficient, and most doctors and nurses could only perform surrounding vein puncture, and debridement and suture surgery. They could not recue critically ill patients alone. ConclusionsFor these township hospitals, the service radius is too long, the number of doctors and nurses is too small, and the ability of service is insufficient. In order to meet the demand of emergency resources in ethnic areas as far as possible, we should increase investment and promote medical devices, increase the number of doctors and nurses, improve the personnel structure, and strengthen professional training.
ObjectiveTo investigate the correlation between rapid emergency medicine score (REMS) and therapeutic intervention scoring system (TISS-28) score and analyze the feasibility of assessing the nursing workload by REMS score for critically wounded earthquake victims, in order to provide reference for rapid and effective resource allocation for earthquake victims. MethodsA retrospective analysis was carried out on 39 Lushan earthquake victims with their acute plysiology and chronic health evaluationⅡ scores higher than 25, who were directly transferred from the earthquake site to the Emergency Department of West China Hospital between April 20 and 27, 2013. Among them, there were 24 males and 15 females aged between 5 and 90 years old averaging (57.1±19.8) years. REMS score and TISS-28 score were calculated for each victim. The relationship between REMS score and TISS-28 score was analyzed by correlation analysis and curve estimation including linear model, quadratic model, composite model, growth model, logarithm model, cubic model and exponential model. Then, we tried to find out the most suitable description for the relationship between REMS score and TISS-28 score. ResultsThe Spearman correlation coefficient between the two score systems was 0.710 and the most suitable description for the relationship between REMS score and TISS-28 score was logarithmic curve model. The formula was TISS=-5.946+4.467lnREMS. ConclusionREMS score can be applied as a nursing workload predicting tool for critically wounded victims in Lushan earthquake and it provides a guidance for rational allocation of health resources.
With the innovation and breakthrough of key technologies in smart medicine, actively exploring smart emergency measures and methods with artificial intelligence as the core technology is helpful to improve the ability of emergency medical team to diagnose and treat acute and critical diseases. This paper reviews the application status of artificial intelligence in pre-hospital and in-hospital diagnosis and treatment capabilities and system construction, expounds on the challenges it faces and possible coping strategies, and provides a reference for the in-depth integration and development of “artificial intelligence + emergency medicine” education, research and production during the new wave of scientific and technological revolution.