Journal of Preventive Medicine and Public Health

The Korean Society for Preventive Medicine (대한예방의학회)
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Efficient DRG Fraud Candidate Detection Method Using Data Mining Techniques

데이터마이닝 기법을 이용한 효율적인 DRG 확인심사대상건 검색방법

  • Lee, Jung-Kyu (Department of Health Policy and Management, Seoul National University College of Medicine) ;
  • Jo, Min-Woo (Department of Preventive Medicine, College of Medicine, University of Ulsan) ;
  • Park, Ki-Dong (Department of Health Policy and Management, Seoul National University College of Medicine) ;
  • Lee, Moo-Song (Department of Preventive Medicine, College of Medicine, University of Ulsan) ;
  • Lee, Sang-Il (Department of Preventive Medicine, College of Medicine, University of Ulsan) ;
  • Kim, Chang-Yup (Graduate School of Public Health, Seoul National University) ;
  • Kim, Yong-Ik (Department of Health Policy and Management, Seoul National University College of Medicine) ;
  • Hong, Du-Ho (Department of Health Policy and Management, Seoul National University College of Medicine)
  • 이중규 (서울대학교 의과대학 의료관리학교실) ;
  • 조민우 (울산대학교 의과대학 예방의학교실) ;
  • 박기동 (서울대학교 의과대학 의료관리학교실) ;
  • 이무송 (울산대학교 의과대학 예방의학교실) ;
  • 이상일 (울산대학교 의과대학 예방의학교실) ;
  • 김창엽 (서울대학교 보건대학원) ;
  • 김용익 (서울대학교 의과대학 의료관리학교실) ;
  • 홍두호 (서울대학교 의과대학 의료관리학교실)
  • Published : 2003.06.01
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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 peformed 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.

Keywords

References

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