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KMID : 0371020030360020147
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Journal of Preventive Medicine and Public Health
2003 Volume.36 No. 2 p.147 ~ p.152
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Efficient DRG Fraud Candidate Detection Method Using Data Mining Techniques
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Hong Du-Ho
Lee Jung-Kyu
Jo Min-Woo
Park Ki-dong
Lee Moo-Song
Lee Sang-Il
Kim Chang-Yup
Kim Yong-Ik
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Abstract
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OBJECTIVES: To develop a Diagnosis-Related Group (DRG) fraud candidate detection method, using data mining techniques, and to examine the efficiency of the developed method.
METHODS: The study included 79,790 DRGs and their related claims of 8 disease groups (Lens procedures, with or without, vitrectomy, tonsillectomy and/or adenoidectomy only, appendectomy, Cesarean section, vaginal delivery, anal and/or perianal procedures, inguinal and/or femoral hernia procedures, uterine and/or adnexa procedures for nonmalignancy), which were examined manually during a 32 months period. To construct an optimal prediction model, 38 variables were applied, and the correction rate and lift value of 3 models (decision tree, logistic regression, neural network) compared. The analyses were performed separately by disease group.
RESULTS: The correction rates of the developed method, using data mining techniques, were 15.4 to 81.9%, according to disease groups, with an overall correction rate of 60.7%. The lift values were 1.9 to 7.3 according to disease groups, with an overall lift value of 4.1.
CONCLUSIONS: The above findings suggested that the applying of data mining techniques is necessary to improve the efficiency of DRG fraud candidate detection.
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KEYWORD
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Diagnosis-Related Groups, Fraud, Decision trees, Neural networks
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