Efficient DRG Fraud Candidate Detection Method Using Data Mining Techniques / 예방의학회지
Korean Journal of Preventive Medicine
;
: 147-152, 2003.
Artigo
em Coreano
| 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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Apendicectomia
/
Tonsilectomia
/
Árvores
/
Vitrectomia
/
Árvores de Decisões
/
Adenoidectomia
/
Modelos Logísticos
/
Cesárea
/
Grupos Diagnósticos Relacionados
/
Mineração de Dados
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
/
Fatores de risco
Limite:
Gravidez
Idioma:
Coreano
Revista:
Korean Journal of Preventive Medicine
Ano de publicação:
2003
Tipo de documento:
Artigo
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