Reducing abnormal expenses in national health insurance based on a control chart and decision tree-driven define, measure, analyze, improve and control process.

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.

Training Effects and Intelligent Evaluated Pattern of the Holistic Music Educational Approach for Children with Developmental Delay.

This study focuses on the holistic music educational approach for young children developed by music therapists and experts, which combines technology with music, integrates it into the local culture, and frameworks it for holistic education. This method includes 231 children in Taiwan's early intervention education system for children with developmental delay. The learning occurs twice a week for 40 min over 32 weeks. The results show that implementing the holistic music educational approach can significantly improve children's ability with developmental delay and that supportive training has a positive effect. In addition, the decision tree explores and develops an intelligently evaluated pattern with highly effective learning. This model has a sensitivity rate of 90.6% on the in-sample, and the comprehensive indicator F is 79.9%, so it has a high reference value. In the future, those involved in education will be able to use the data mining to use the auxiliary decision-making system as an assessment tool for young children participating in education pre- and midterm of the course, to prejudge its continued implementation and learning effectiveness, to decide whether to continue to invest in and adjust the curriculum, and to make more effective use of educational resources.