## 5 Discussion  
The model established in this study can be used to predict the probability that a medical claim with item weights will be deducted during professional audit. Through the preliminary analysis of the system, if a claim is identified as having a low probability of being denied or weight deduction, the claim could be sent to the committee of TNHIB without further review. Otherwise, the claim must be reviewed and modified based on the system's suggestions before sending to the committee, to prevent claim denial. This study finds that not only the proposed system could provide modification suggestions pertinent to all weight deduction, but also a rule base for record writing guidance could give critical suggestions to increase the chance of passing the professional audit.  
In this study, the topics explored by the LDA and medical treatments are used as the variables for the logistic regression model. The coefficients of the variables are used to analyze the topics, diagnostic results, and the influence of drugs on the denied medical claims. A variable with a positive coefficient indicated the existence of the variable can increase the probability of weight deduction, while a variable with a negative coefficient indicates the reverse effect. For example, the variables in E11, except Topic 2, all show  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Morbid Entity** & \begin{tabular}{c} **The Proposed** \\ **Solution Approach** \\ \end{tabular} & \
\begin{tabular}{c} **Traditional Manual** \\ **Approach** \\ \end{tabular} \\ \hline \multirow{2}{*}{\% Records to be fixed} & E11 & 41.7\% & Less than 10\% \\ \cline{2-4}  & N18 & 89.3\% & Less than 10\% \\ \cline{2-4}  & K21 & 90.3\% & Less than 10\% \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Amount money to be \\ saved * \\ \end{tabular} } & \textgreater{}NT 300 thousands & Less than NT \\  & \textless{} dollars & thousand dollars \\ \hline \hline \end{tabular}  
\end{table}
Table 4: The comparison of the proposed solution approach to traditional manual approach.  
Figure 12: E11 logistic regression.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Morbid Entity** & **AUC** & **Recall** \\ \hline E11 & 0.782 & 0.955 \\ \hline N18 & 0.776 & 0.955 \\ \hline K21 & 0.852 & 0.849 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model Performance.  
significantly positive/negative to deducted or not. The topic model plays an important role in the perdition modeling.  
In addition, the results regarding Disease I669 diagnosis shows that medical records M5116 are at greater risk of weight deduction. In this respect, it is recommended that doctors provide more supporting information when writing medical records that contain such topics, drugs and diagnoses while little information is related to main diagnosis code or requires high cost of drugs and examinations.  
Moreover, it was found that the disease classification code (Codes for International Classification of Diseases II) is also significant to the probability. It can be speculated that the disease classification code exerts an impact on the audit committee's approval of a claim. Apparently, only when a known disease classification exists the symptoms of a disease can be described or recorded properly based on the doctor's diagnosis.