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Korean Journal of Preventive Medicine 2003;36(2): 147-152.
Efficient DRG Fraud Candidate Detection Method Using Data Mining Techniques.
Duho Hong, Jung Kyu Lee, Min Woo Jo, Kidong Park, Sang Il Lee, Moo Song Lee, Chang Yup Kim, Yong Ik Kim
1Department of Health Policy and Management, Seoul National University College of Medicine, Korea.
2Department of Preventive Medicine, College of Medicine, University of Ulsan, Korea.
3Graduate School of Public Health, Seoul National University, Korea.
ABSTRACT
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.
Key words: Diagnosis-Related Groups; Fraud; Decision trees; Neural networks
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