Objective To find the approximate error factors influence of dosing, fundamentally reduce the administra-tion of approximate error. Methods According to the three level of hospital administration approximation error of 181 cases were retrospectively analyzed. Results The following factors were caused drug similar mistakes.Administration of the approximation error incidence decreased year by year during the investigation. Medication errors and errors in the approximation, error dose administered in the proportion accounted for more than 60%. More than 90% administration of the approximation error occurs during the day. The title, length of service and equipment have important effects on reducing drug approximation error, do not comply with the work flow, violating the operating rules, because of inter-ference interrupts the continuity, the lack of communication, carelessness, memory errors, drug similarity, difference signal information system. Conclusion Specification for dug management system, the administration management as a whole,the full implementation of the"Five Rights"management.

Objective To explore the risk factors for the occurrence of hypertension in middle-aged and elderly residents in China using the Cox regression analysis model and decision tree model, and compare the differences between the two methods. Methods The 2011-2015 China Health and Retirement Longitudinal Study data were used. The study investigated the risk factors for hypertension using both a multivariate Cox regression model and a decision tree model. Results The results showed that the incidence rate of hypertension between 2011-2015 was 22.79%. Both the Cox regression model and decision tree model identified age, education level, body mass index, and diabetes as risk factors for hypertension. The Cox regression model also identified drinking status as a risk factor, while the decision tree model identified gender and marital status as additional risk factors. The area under the curve (AUC) suggested that the Cox regression model and decision tree model had comparable ability to predict hypertension. Conclusions The risk factors for hypertension include gender, age, education level, marital status, alcohol consumption, body mass index, and history of diabetes. The effectiveness of the hypertension prediction model established based on Cox regression model and decision tree model results is not different.