Evaluation of inpatient services of tertiary comprehensive hospitals based on DRG payment.

Objective: This study aims to evaluate inpatient services in 49 tertiary comprehensive hospitals using indicators from the diagnosis related groups (DRG) payment system. Method: DRG data from 49 tertiary comprehensive hospitals were obtained from the quality monitoring platform for provincial hospitals, and relevant indicators were identified. The analytic hierarchy process (AHP) was used to compute the weight of each indicator. The rank sum ratio method was used to calculate the weight rank sum ratio (WRSR) value and the corresponding probit value of each hospital. The hospitals were divided into four grades based on the threshold value: excellent, good, fair, and poor. Results: Eight indicators of the 49 hospitals were scored, and the hospital rankings of indicators varied. The No. 1 hospital ranked first in the indicators of "total number of DRG", "number of groups", and "proportion of relative weights (RW) ≥ 2". The WRSR value of the No.1 hospital was the largest (0.574), and the WRSR value of the No. 44 hospital was the smallest (0.139). The linear regression equation was established: WRSRpredicted =-0.141+0.088*Probit, and the regression model was well-fitted (F = 2066.672, p < 0.001). The cut-off values of the three WRSRspredicted by the four levels were 0.167, 0.299, and 0.431, respectively. The 49 hospitals were divided into four groups: excellent (4), good (21), average (21), and poor (3). There were significant differences in the average WRSR values of four categories of hospitals (p < 0.05). Conclusion: There were notable variances in the levels of inpatient services among 49 tertiary comprehensive hospitals, and hospitals of the same category also showed different service levels. The evaluation results contribute to the health administrative department and the hospital to optimize the allocation of resources, improve the DRG payment system, and enhance the quality and efficiency of inpatient services.

Diagnostic Significance of DCE-MRI-Related Quantitative Parameters for Nasal Cavity and Sinus Tumors.

Objective: Our aim was to explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-related quantitative parameters for benign and malignant nasal cavity and sinus tumors. Methods: A total of 78 patients with nasal sinus tumors admitted to People's Hospital of Qingdao Chengyang District in China were enrolled in our study, Of the patients, 41 were diagnosed as having benign tumors and 37 as having malignant tumors by pathological diagnosis. All patients received DCE-MRI scans before surgery to derive time-intensity curves (TICs) and related quantitative parameters (flux rate constant [Kep], transfer constant [Ktrans], extravascular volume fraction [Ve]). The diagnostic surgical pathology results were used as the gold standard to analyze the diagnostic effect of TIC and related quantitative parameters of DCE-MRI, and the receiver operating characteristic (ROC) curves were plotted to determine the values of each parameter in predicting nasal sinus tumors. Results: The percentage of class I in the benign group was significantly higher than in the malignant group (P < .05); the percentage of class III in the benign group was significantly lower than in the malignant group (P < .05); the percentage of class II in the 2 groups was comparable (P > .05). Kep, Ktrans and Ve in the benign group were 0.338±0.124, 0.061±0.035 and 0.532±0.138, respectively; Kep, Ktrans and Ve in the malignant group were 0.785±0.211, 0.441±0.125 and 0.327±0.048, respectively. The levels of Kep and Ktrans were significantly lower in the benign group than in the malignant group (all P < .05); the levels of Ve were significantly higher in the benign group than in the malignant group (P < .05). The optimal Kep cut-off value for predicting malignant nasal sinus tumors was 0.510 min-1, with a sensitivity of 81.4% and specificity of 89.5%; the optimal Ktrans cut-off value for predicting malignant nasal sinus tumors was 0.206 min-1, with a sensitivity of 84.3% and specificity of 89.7%; the optimal Ve cut-off value for predicting malignant nasal sinus tumors was 0.384 min-1, with a sensitivity of 71.8% and specificity of 82.4%. Conclusion: DCE-MRI-related quantitative parameters are ideal for the diagnosis of benign and malignant nasal sinus tumors. This modality provides more data for the identification of the nature of the tumor, and thus merits clinical promotion and application.