Browse > Article

Estimation of a Nationwide Statistics of Hernia Operation Applying Data Mining Technique to the National Health Insurance Database  

Kang, Sung-Hong (School of Health Administration, Inje University)
Seo, Seok-Kyung (Medical Record & Informatics Team, Asan Medical Center)
Yang, Yeong-Ja (Department of Preventive Medicine, Cheju National University College of Medicine)
Lee, Ae-Kyung (National Health Insurance Corporation)
Bae, Jong-Myon (Department of Preventive Medicine, Cheju National University College of Medicine)
Publication Information
Journal of Preventive Medicine and Public Health / v.39, no.5, 2006 , pp. 433-437 More about this Journal
Abstract
Objectives: 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.
Keywords
Estimation techniques; Logistic models; Predictive value of tests; Computer data processing; Korea Health Insurance Cooperation;
Citations & Related Records

Times Cited By SCOPUS : 0
연도 인용수 순위
  • Reference
1 Cho GL, Jo DR, Lee SL. Theory and Exercise for Data Mining. Chung Goo. 2001
2 Judith H. Hibbard, Ellen P. Supporting Informed customer health care decision: Data presentation approaches the facilitae the sue of information in choice. Ann Rev Public Health 2003; 24: 413-433   DOI   ScienceOn
3 Kang HC, Han ST, Che JH, Lee SK, Kim YS, Yem YH, Kim MK. Data Mining Using SAS Enterprise Miner 4.0 -Methodology and Application - Third Edition. Freedom Academy, 2001
4 Sung JY, Kang SH, Kim ON, Research for Surgical Operations Management and Constmcting DB for Major Surgical Operations in NHIC. National Health Insurance Cooperation, 2003
5 Lee Ak, Jung HJ, Park IS. Development of a Health Management Model for Tailored Health Information Service using the Data Mining Technique. National Health Insurance Cooperation, Health Insurance Research Center, 2004
6 Greg R, Ellen J. Data Mining for Health Care Quality Improvement SAS institute, Gary, NC, 1998
7 Jang YS, Doh SR, Ko KH, Seo JS, Seo SW, Bu YK, Lee LH. A Study of Health Data Production: OECD Comparison and Improvement Strategies. Ministry of Health Welfare Korea Institute of Health and Social Affairs, 2003
8 Song TM, Lee YH, Lee GH, Lim GY, Cha YM, Jung SY, Lee CK. A Study for Establish and Application of Database in NHIC of the based Knowledge. Korea Institute of Health and Social Affairs, 2002
9 Jang YS, Ge HB, Doh SR, Go KH, Sea JS, Seo SW, Bu YK. A Study on Health Promotion Statistics in 2000 OECD. Ministry of Health welfare, 2000
10 Korea Health Industry Development A Study on Statistics Index Development using Medical Insurance Data. Ministry of Health welfare, 1999
11 Bae HS, Cho DH, Seok KH, Kim BS, Choi KL, Lee JY, No SY, Lee SC, Sohn YH. Data Mining Using SAS Enterprise Miner. Kyow Oosa, 2004
12 Bae JM, Kang SH, Kim ON, Park BJ. Statistics for Major Surgical Operations in the National Health Insurance, 2004 by Constructing DB for Major Surgical Operations. National Health Insurance cooperation, 2005 (Korean)
13 Hong DH, Lee JG, Jo My, Park GD, Lee MS, Lee SI, Kim CY, Kim YI. Efficient DRG fraud candidate detection method using data mining techniques. Korean J Prev Med 2003; 26(2): 147-152 (Korean)
14 Park YS, Kang SH, GU BB, Kim BC, Kim YN, KIm WJ, Ru Mi, Song SK, Song HD, Ha JY. Hospital Information Management. Goryea Medical Science, 2005
15 Lee SM, Kang JO, Se YM. Predictive model of cancer patient's medical care expense using data mining. J Korea Soc Manage Inform Syst 2003; 1: 664-671
16 Jang YS, Ko YH, Doh SR, Lee LH, Seo SW. Study on the Production of 2004 Health Data in OECD. Ministry of Health welfare Korea Institute of Health and Social Affairs, 2004
17 Kim YM, Park IL, Kang SH. Development of HRA of hypertension using data of NHIC data. J Health Inform Institute, 2004; 10: 47-56 (Korean)