This study examined the cost of medical insurance for "sepsis" treatment in Taiwan. We applied statistical tests, cost control charts, and C5.0 decision trees using the define, measure, analyze, improve and control (DMAIC) process to mine data on Diagnosis-Related Groups (DRGs) and clinics that reported expense anomalies and disposal costs. Analyzing 353 valid samples (application fees) from four DRGs, 70 clinics, and 15 input variables, abnormalities in application fees for adults (age ≧18 years old) with comorbidities or complications was significant (95% confidence interval) in one DRG and nine clinics. Four input variables (ward charge, treatment fee, laboratory fee, and pharmaceutical service charge) had a significant impact. Improvements or controls should be prioritized for three clinics (Nos. 49, 44, and 14) and two input variables (treatment and laboratory fees). This model can be replicated to ascertain excess medical expenditures and improve the efficiency of medical resource use.
Keywords: data mining; decision tree; define, measure, analyze, improve and control; diagnosis related groups; national health insurance.