Objective
To investigate drug usage and costs of children inpatients with bronchopneumonia in Karamay Central Hospital in 2014 and to provide baseline for evidence-based pharmacy study of single disease in respiratory system.
Methods
The information of drug use and expenditure of children inpatients with bronchopneumonia were collected from the hospital information system (HIS). We analyzed the data including frequency, proportion and cumulative proportion by Excel 2007 software.
Results
A total of 890 children inpatients were included, the average age was 1.00±2.17 years old. Among the antibiotics of single therapy, the frequency of amoxicillin and clavulanate potassium for injection was highest. Among the antibiotics of combination therapy, the frequency of macrolides was highest.
Conclusion
The mainly drugs for treatment of children inpatients with bronchopneumonia in Karamay Central Hospital in 2014 is amoxicillin and clavulanate potassium for injection.
ObjectiveTo predict the total hospitalization expenses of bronchopneumonia inpatients in a tertiay hospital of Sichuan Province through BP neural network and support vector machine models, and analyze the influencing factors.MethodsThe home page information of 749 cases of bronchopneumonia discharged from a tertiay hospital of Sichuan Province in 2017 was collected and compiled. The BP neural network model and the support vector machine model were simulated by SPSS 20.0 and Clementine softwares respectively to predict the total hospitalization expenses and analyze the influencing factors.ResultsThe accuracy rate of the BP neural network model in predicting the total hospitalization expenses was 81.2%, and the top three influencing factors and their importances were length of hospital stay (0.477), age (0.154), and discharge department (0.083). The accuracy rate of the support vector machine model in predicting the total hospitalization expenses was 93.4%, and the top three influencing factors and their importances were length of hospital stay (0.215), age (0.196), and marital status (0.172), but after stratified analysis by Mantel-Haenszel method, the correlation between marital status and total hospitalization expenses was not statistically significant (χ2=0.137, P=0.711).ConclusionsThe BP neural network model and the support vector machine model can be applied to predicting the total hospitalization expenses and analyzing the influencing factors of patients with bronchopneumonia. In this study, the prediction effect of the support vector machine is better than that of the BP neural network model. Length of hospital stay is an important influencing factor of total hospitalization expenses of bronchopneumonia patients, so shortening the length of hospital stay can significantly lighten the economic burden of these patients.
