Efficient DRG Fraud Candidate Detection Method Using Data Mining Techniques / 예방의학회지
Korean Journal of Preventive Medicine
;
: 147-152, 2003.
Article
in Korean
| WPRIM
| ID: wpr-119082
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Appendectomy
/
Tonsillectomy
/
Trees
/
Vitrectomy
/
Decision Trees
/
Adenoidectomy
/
Logistic Models
/
Cesarean Section
/
Diagnosis-Related Groups
/
Data Mining
Type of study:
Diagnostic study
/
Prognostic study
/
Risk factors
Limits:
Pregnancy
Language:
Korean
Journal:
Korean Journal of Preventive Medicine
Year:
2003
Type:
Article
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