| Summary: | 博士 === 國立政治大學 === 資訊管理研究所 === 100 === There are five sources of illegal medical cases checked by BNHI (Bureau of National Health Insurance): reported by public, reported by the operator of insured unit, unusual findings while auditing expenses, special case audit, and unusual findings for returned health insurance cards. The abnormal medical institutions can only be found out by analyzing digital data in the source of auditing expenses. However, the digital data can only detect whether the physicians’ service deviates from the normal distribution (excessive unnecessary service offered by some physicians or hospitals), instead of the false claim of medical expenses and fraud behavior.
Thus, by reviewing a lot of fraud literature, this study finds the best example in Bolton &; Hand (2002) is digit analysis of Benford's Law. Benford's Law points that the smaller the first digit is, the more frequent the digit shows, vice versa. In recent years, Benford's law has been applied in fraud review process in different fields.
So far no specific article has applied Benford's Law in the research related to the BNHI medical expenses, so we did a study using inpatient (total) and outpatient (sampling) data from 1999 to 2003. There are three steps in this study: 1. Overall empirical study of all inpatient and outpatient medical institutions. 2. Try to find out the unusual medical institutions only using digit analysis. 3. Find out a smart anomaly detection model and verify its effectiveness.
There are three conclusions of the study:
1. For the health insurance expenses applied by the BNHI-contracted inpatient institutions, the frequency of the first digit accords with Benford's Law, while the second, third, and fourth digits does not accord with Benford's Law. For the general health insurance expense, the frequencies of the first, second, third, and fourth digit accord with Benford's Law. While only the first digit meets Benford's Law for cases paid by disease, as its special pricing method causes the different frequencies of the second, third, and fourth digits of health insurance expenses.
2. This study shows that the digit analysis of Benford's Law does contribute to find out the abnormal institutions, but also pay the price of misidentify the normal institutions. By using chi-square test and Cramer's V statistics method, the low discrimination rates of both inpatient and outpatient hospitals leads to poor overall accuracy. It suggests that the simple method of digit analysis is insufficient to test the abnormal institutes, and further investigation with other tools is requested.
3. This study establishes a smart anomaly detection model of health insurance expense, which is based on variable selection with GHSOM neural networks to identify the optimal model, and then uses RBFNN (radial basis function neural network), GRNN (general regression neural network), and ERNN (Elman recurrent neural network) to predict the abnormal institutions. Comparing RBFNN, GRNN and ERNN with the stepwise logistic regression model as the Benchmark, the study concludes that the linear relationship between derived indicator of Benford's Law and abnormal/normal institutions exits. Therefore, we can predict by logistic regression model and verify by neural network model.
The study intends to apply the smart technology of Benford's Law to the large-scale preliminary review of the National Health Insurance database, which can help to identify the sources of the anomaly expenses of medical institutions and find out the fraud ones. Therefore, the decreasing fraud of medical institutions will cut down the health insurance expense for financial break-even. We hope we can contribute to the sustainable development of health insurance.。
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