Objective To assess the patient enablement level in a general hospital and its related factors,to investigate the relationship of patient enablement with patient's overall satisfaction and doctor's empathy levels.Methods Patients attending the Family Medicine Clinic of HKU-Shenzhen Hospital in September 2014 and met the inclusion criteria were asked to complete a questionnaire survey which included general characteristics,overall satisfaction;the Patient Enablement Instrument (PEI) and the Consultation and Relational Empathy Measure Scale (CARE) after the consultation.Results Total 2 326 questionnaires were issued and 1 818 were retrieved,of which 1 478 were qualified questionnaires.The qualified rate of PEI was 81.3％ and the qualified rate of patient overall satisfaction was 96.28％.The positive rate of patient enablement was 97.70％ (1 444/1 478),and the mean score of PEI was 8.21 ±3.33.Multivariate linear regression analysis showed that the PEI scores were not influenced by gender and education level of the patients,gender of the consulting doctors and initial/follow up consultation.The PEI scores were influenced by the age of the patients,the types of clinic (general practice/chronic practice clinic) and the reasons for consulting (P ＜ 0.05).There were correlations between PEI score and overall satisfaction of patients (r =0.383,P ＜ 0.001),patient's recommendation of the clinic (r =0.595,P ＜ 0.001) and CARE scores (r =0.546,P ＜ 0.001).Conclusion The results show that the PEI scores of patients visiting family medicine clinic of this hospital are higher than those reported in other studies.The PEI score may be influenced by the age of the patients,the types of clinic and the reasons for consulting.Positive correlations between patient enablement and the overall satisfaction,patient's recommendation and doctor empathy were observed.

Objective To assess the empathy level of general practitioners (GPs) in the outpatient clinics of a general hospital,and to investigate the related factors.Methods Patients attending the Family Medicine Clinic of HKU-Shenzhen Hospital in September 2014 and met the inclusion criteria were asked to complete the questionnaire which included general characteristics,overall satisfaction and the Measure-Consultation and Relational Empathy Measure (CARE) Scale after the consultation.Results Of the 1 818 questionnaires retrieved,1 690 CARE scales were completed (intact rate was 92.96%).The overall satisfaction rate was 95.92% (1 621/1 690).And 97.28% (1 644/1 690) of the patients would recommend family medicine service to their friends or relatives.The total score of CARE scale was (45.47±6.26),and the scores of 4 CARE components were (4.63 ± 0.59),(4.43 ± 0.81),(4.54 ± 0.69) and (4.55 ± 0.66),which showed significant difference (P<0.01).The multivariate linear regression analysis showed that the mean CARE scores were not influenced by gender and education levels of the patients,gender of the consulting doctors,initial/follow up consultation,and the location of the consultation.The mean CARE scores were influenced by age of patients and the reasons for consulting (P<0.05).There was a moderate correlation of the CARE scores with the overall satisfaction of patients (r=0.613,P<0.001) and patient's recommendation of the clinic (r=0.466,P<0.001).Conclusion Doctors who were evaluated in this study have a higher empathy level than results from other countries.There is positive correlation between doctor's empathy level and patient's overall satisfaction.The result of doctor's empathy level may be influenced by patient's age and the reasons for consulting.

Objective To evaluate the effect of Critical Incident Reporting System on the quality of clinical anesthesia.Methods Anesthesia-related critical incidents happened in the perioperative period were reported in voluntary,anonymous,no punishment and confidential manners.The data was collected,classified and documented by assigned professionals on a regular basis from September 2012 to August 2016.The critical incidents were retrospectively analyzed after the risk was assessed.The 4-year reporting rate was collected.The risk of critical incidents was assessed using severity and probability analysis,and the critical incidents-inducing risk factors were analyzed.Results The 4-year reporting rate of critical incidents was 0.551％.From 1st to 4th year,the reporting rates were 0.729％,0.598％,0.819％ and 0.368％,respectively,and the incidence of injury incidents was 0.112％,0.106％,0.133％ and 0.031％,respectively.The reporting rate of critical incidents and incidence and reporting rate of the injury incidents showed a decreasing trend for 1st and 2nd year,significantly increased for 3rd year and decreased for 4th year (P＜0.05).The first three critical incident categories were equipment use and respiratory system-and workflowrelated incidents.Patient injury during surgery was considered an extremely high risk incident;the factor of the medical staff in the department of anesthesiology is the first critical incidents-inducing risk factor.Conclusion Critical Incident Reporting System can discover and correct the system-related risk and the inducing factors in the department of anesthesiology and is an effective method of improving the service quality of clinical anesthesia.

Abstract According to the Healthy China Action Plan, Wuhan gives full play to the role of preventing and controlling student myopia by promoting student health. The primary focus is placed on education in schools, and Wuhan has integrated educational resources to develop a multi-level myopia prevention and control system and service network for school students. The network contains educational adminstrative, schools, families, and professional technical service organizations. By integrating multiple disciplines, Wuhan has built a comprehensive vision health management service system for all students. The Internet and cloud intelligent monitoring facilitated the establishment of a smart vision health management platform for students, which thoroughly and efficiently implemented myopia prevention and control to safeguard students visual health by engaging in education, monitoring, and supervision. The prevention and control of student myopia is a breakthrough for comprehensive healthy development of students. A comparison of the standard myopia rate in Wuhan in 2019 and 2018 revealed that the standard myopia rate at different learning stages of primary school, junior high school, and high school dropped by 3.31, 2.50, and 2.26 percentage points, respectively, and the rate of myopia in primary school was significantly lower than the national level. Post-epidemic surveys showed that the compliance rate and the awareness rate of the visual environment and visual behaviors of primary and secondary school students in Wuhan reached more than 80%, and prevalence of newly onset myopia or decreased vision was 30%, which was lower than the national average. The "Wuhan Model" provides an important referential framework for public health services for school students.

Objective:To formulate surgical strategies and guide the implementation of laparoscopic anatomical hepatectomy with preoperative simulative resection.Methods:Twenty-two cases of hepatocellular carcinoma undergoing laparoscopic lobe, segment, subsegment and combined segment liver resection following preoperative simulative resection from Sep 2020 to Jan 2022 were enrolled in this study retrospectively.We observed and analyzed the operation time,intraoperative blood loss,postoperative hospital stay and postoperative complication.Results:All patients underwent laparoscopic hepatectomy successfully according to the preoperative simulative resection plan without conversion, some of them adjusted plan according to preoperative simulative resection. The median operation time was 170.0 min, the median intraoperative blood loss was 150.0 ml, the median times of pringle maneuver was done on 4 episodes, and the median postoperative hospital stay was 6.5 days. There were no severe postoperative complications in all cases.Conclusion:Preoperative simulative resection can plan the range of surgical resection accurately by visualizing important anatomical structures,greatly helping the actual surgical hepatectomy.

BACKGROUND:Based on different algorithms of machine learning,how to carry out clinical research on lumbar disc herniation with the help of various algorithmic models has become a trend and hot spot in the development of intelligent medicine at present. OBJECTIVE:To review the characteristics of different algorithmic models of machine learning in the diagnosis and treatment of lumbar disc herniation,and summarize the respective advantages and application strategies of algorithmic models for the same purpose. METHODS:The computer searched PubMed,Web of Science,EMBASE,CNKI,WanFang,VIP and China Biomedical(CBM)databases to extract the relevant articles on machine learning in the diagnosis and treatment of lumbar disc herniation.Finally,96 articles were included for analysis. RESULTS AND CONCLUSION:(1)Different algorithm models of machine learning provide intelligent and accurate application strategies for clinical diagnosis and treatment of lumbar disc herniation.(2)Traditional statistical methods and decision trees in supervised learning are simple and efficient in exploring risk factors and establishing diagnostic and prognostic models.Support vector machine is suitable for small data sets with high-dimensional features.As a nonlinear classifier,it can be applied to the recognition,segmentation and classification of normal or degenerative intervertebral discs,and to establish diagnostic and prognostic models.Ensemble learning can make up for the shortcomings of a single model.It has the ability to deal with high-dimensional data and improve the precision and accuracy of clinical prediction models.Artificial neural network improves the learning ability of the model,and can be applied to intervertebral disc recognition,classification and making clinical prediction models.On the basis of the above uses,deep learning can also optimize images and assist surgical operations.It is the most widely used model with the best performance in the diagnosis and treatment of lumbar disc herniation.The clustering algorithm in unsupervised learning is mainly used for disc segmentation and classification of different herniated segments.However,the clinical application of semi-supervised learning is relatively less.(3)At present,machine learning has certain clinical advantages in the identification and segmentation of lumbar intervertebral discs,classification and grading of the degenerative intervertebral discs,automatic clinical diagnosis and classification,construction of the clinical predictive model and auxiliary operation.(4)In recent years,the research strategy of machine learning has changed to the neural network and deep learning,and the deep learning algorithm with stronger learning ability will be the key to realizing intelligent medical treatment in the future.