Objective To learn the awareness rate of patients for dual referral and analyze factors affecting their intent, in order to provide references for healthcare decision making. Method Data were collected with questionnaires, and analyzed with logistic regress model. Results (1) Factors for patients' awareness rate: Patients' attitude toward first treatment in community) accessibility to community healthcare centers in their vicinity. (2) Factors affecting patients' intention for dual referral: Patients' attitude toward dual referral feasibility; patients' attitude toward rehabilitation back to community if the community joins hands with a hospital; whether patients would choose the community for treatment if the community offers benefits. Conclusion Greater efforts are expected to encourage the people to embrace the practices of dual referral and first treatment in community, and to support community healthcare centers, in addition to expanding the cooperation between such centers and hospitals.

The utilization of mobile library users in Shanxi Medical University and its First and Second Affiliated Hospitals was investigated. The needs of mobile library users and the factors influencing the utilization of mobile li-brary were analyzed with strategies proposed for perfecting the construction of mobile library.

Objective To explore the machine learning model and risk factor analysis for hospital infection caused by carbapenem-resistant enterobacteriaceae(CRE).Methods The clinical data of totally 451 patients infected with extended-spectrum β-lactamases(ESBL)producing Enterobacteriaceae treated in the hospital from 2018 to 2022 were retrospectively collected.The patients were divided into CRE group(115 cases)and sensitive group(336 cases)according to the susceptibility of carbapenem.Four machine learning methods in-cluding Logistic regression analysis,random forest,support vector machine,and neural network were used to build prediction models and receiver operating characteristic curve was used to evaluate.Based on the predic-tion model with the best performance,risk factors for CRE infection were analyzed.Results Random forest model had the best performance,with the area under the curve of 0.952 3.The risk factors for predicting CRE infection by the random forest model included 15 clinical data items,namely fever for more than 3 days,cere-bral injury,drainage fluid sample,trunk surgery,first-level or special-level nursing,ICU treatment,procalcito-nin,anti-anaerobic bacteria,the use of third-generation cephalosporins,age,pre-albumin,creatinine,white blood cell count,and albumin.Conclusion The CRE prediction model developed in this study has good predic-tive value and the risk factors have guiding significance for the early prevention and treatment of CRE infec-tion in clinical practice.