ObjectiveTo construct digital management system from the perspective of nursing management which showed nursing information view by integrating nursing data information.Methods The management modules of system construction included archival information of nurses,head-nurse handbook,quality of nursing,nursing shift-arrangement,satisfaction survey,nursing adverse events,nursing sensitive indicators and so on. The subsystem data including hospital personnel management,mobile nurse station,doctor's advice system and so on were shared with the help of a hospital information management platform. The sum,report time,time of transmission and response time of departments of adverse events analyzed by SPSS 19 were compared before(2014) and after(2015) applying management system.Results The report time,time of transmission,response time of measures of adverse events obviously shortened with significant differences after applying nursing management system(P<0.05).Conclusions The application of nursing management system makes data be obtained simply, data sources objective,transmission cycle short,related data be shared simultaneously by departments. The traditional manual statistical method is replaced by the function of data statistics and analysis of the system. The data sensitivity of management department improves. It also provides data support for decision-making. Moreover, job objective in the continual improvement of nursing quality was basically realized.

Objective:To explore the feasibility of extracting the key plane of the normal fetal palate on the 11-13 + 6 week from tomography ultrasonography imaging based on artificial intelligence. Methods:The fetal volume datas of 235 cases of 11-13 + 6 week normal fetal were collected from the Department of Ultrasound in the Luohu District People′s Hospital of Shenzhen and Huazhong University of Science and Technology Union Shenzhen Hospital from May 2020 to April 2021. The data acquisition was completed by sonographers A and B by using the GE Voluson E10 color Doppler ultrasound diagnostic instrument. All datas were marked offline by sonographer C. Tomographic imaging was performed on all included data by sonographer D, the tomographic images were saved and the time-consuming was recorded, and the datas of the sonographer group were obtained. The labeled data were randomly divided into the training set and test set for model transfer learning and testing.The 4-fold cross-validation was adopted to record the test set image output by the model and the time consumption to obtain the intelligent group data. A senior sonographer performed image analysis on the two groups of data images. The feasibility of the intelligent model was verified by comparing the score of the plane of retronasal triangle(RTP), the acquisition rate of RTP, the acquisition rate of the fault, and the time-consuming difference between the sonographer group and the intelligent group. Results:①There was no significant difference in the overall distribution of RTP scores between the sonographer group and intelligent group [5 (5, 6) points vs 5 (5, 6) points, Z=0.355, P=0.722]. The RTP acquisition rate of the sonographer group and intelligent group was not statistically significant (78.72% vs 76.60%, χ 2=0.55, P=0.458). The consistency and correlation of RTP obtained by the two groups were high (Kappa=0.645, φ=0.646, both P<0.001). ②The effective layers of the sonographer group were 9 (8, 9) and the intelligent group was 8 (7, 9). The fault acquisition rate of the doctor group was higher than that of the intelligent group (78.72% vs 68.51%, χ 2=12.52, P=0.001). The consistency and correlation of the two groups in obtaining faults were media (Kappa=0.503, φ=0.521, both P<0.001). ③The time-consuming of the intelligent group was significantly lower than that of the sonographer group [1.50 (1.23, 1.75)s vs 26.94 (22.28, 30.48)s, Z=11.440, P<0.001]. Conclusions:This research model can quickly and accurately realize the extraction and tomography of the key plane of the normal fetal palate on the 11-13 + 6 week.