[Estimation of a nationwide statistics of hernia operation applying data mining technique to the National Health Insurance Database].
OBJECTIVE
The aim of this study is to develop a methodology for estimating a nationwide statistic for hernia operations with using the claim database of the Korea Health Insurance Cooperation (KHIC).


METHODS
According to the insurance claim procedures, the claim database was divided into the electronic data interchange database (EDI_DB) and the sheet database (Paper_DB). Although the EDI_DB has operation and management codes showing the facts and kinds of operations, the Paper_DB doesn't. Using the hernia matched management code in the EDI_DB, the cases of hernia surgery were extracted. For drawing the potential cases from the Paper_DB, which doesn't have the code, the predictive model was developed using the data mining technique called SEMMA. The claim sheets of the cases that showed a predictive probability of an operation over the threshold, as was decided by the ROC curve, were identified in order to get the positive predictive value as an index of usefulness for the predictive model.


RESULTS
Of the claim databases in 2004, 14,386 cases had hernia related management codes with using the EDI system. For fitting the models with applying the data mining technique, logistic regression was chosen rather than the neural network method or the decision tree method. From the Paper_DB, 1,019 cases were extracted as potential cases. Direct review of the sheets of the extracted cases showed that the positive predictive value was 95.3%.


CONCLUSIONS
The results suggested that applying the data mining technique to the claim database in the KHIC for estimating the nationwide surgical statistics would be useful from the aspect of execution and cost-effectiveness.
Kang
Sunghong
S
School of Health Administration, Inje University.
Seon
Seok Kyung
SK
Yang
Yeong-Ja
YJ
Lee
Aekyung
A
Bae
Jong-Myon
JM
D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D005260:Female; D059685:Herniorrhaphy; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007723:Korea / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009313:National Health Programs / Q000706:statistics & numerical data*