Objective To Provide statistical references for disease-based payment reform with Diagnosis Related Groups (DRGs). Methods Based on 1 969 stroke inpatients from two hospitals in Chongqing city, we used classification and regression trees (CART) of decision tree to establish classification regulations of the case-mix model for stroke inpatients, and multivariate statistical model to evaluate whether the case-mix could provide a satisfactory prediction to costs for stroke inpatients in comparison with the foreign model. Results ① The classification nodes of our model were surgical procedure, nursing care degree, and hospital infection respectively by which 1 969 stroke inpatients were divided into 5 groups. The classification nodes in foreign model were surgical procedure, age≥50 years, and whether patients would refer to other institutions after leaving the hospitals by which 1 969 stroke inpatients were also classified into 5 groups. ② For medical institutions and the third payers, we found that the data from our model could explain 80.46% of the total costs and 16.58% for individual inpatient, which were higher than that of foreign model (76.87% for medical institutions and the third payers, 9.13% for individuals ). Conclusions Compared with foreign model, our model is more suitable for the situation in China. The study is only based on 1 969 stroke inpatients from south west part of China, so the conclusion needs further studies to confirm.
It's common that general rules exist in a certain classification. The general rules of expense classification enable us to judge the category of a patient as soon as possible and to curb the expense. Theory of rough set helps us reach the best reduction of attributes. Based on the core attributes, classification rules are put forward by value reduction. The results show that 10 core attributes remain in 21 attributes of 1527 inpatients' information and 76 classification rules are founded. All of 76 rules guide classification of the patients. 44 of the 76 rules define the only category of a patient, the other 32 rules defines the potential catagories of a patient. Meanwhile, equal attributes of the same category are summerized to guide the cost control of patients. The results indicate that the theory of rough set is effective in attributes reduction and rule generalization of patient expense classification, and it has important significance on medical practice.