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http://dx.doi.org/10.12811/kshsm.2016.10.3.063

Development of Healthcare Data Quality Control Algorithm Using Interactive Decision Tree: Focusing on Hypertension in Diabetes Mellitus Patients  

Hwang, Kyu-Yeon (Pusan National University Hospital)
Lee, Eun-Sook (Pusan National University Hospital)
Kim, Go-Won (Pusan National University Hospital)
Hong, Seong-Ok (Department of Korea Centers for Disease Control & Prevention)
Park, Jung-Sun (Korea Health Industry Development Institute)
Kwak, Mi-Sook (Korea Health Industry Development Institute)
Lee, Ye-Jin (Korea Health Industry Development Institute)
Lim, Chae-Hyeok (Department of Health Administration, Inje University)
Park, Tae-Hyun (Department of Health Administration, Inje University)
Park, Jong-Ho (Department of Health Administration, Inje University)
Kang, Sung-Hong (Department of Health Administration, Inje University)
Publication Information
The Korean Journal of Health Service Management / v.10, no.3, 2016 , pp. 63-74 More about this Journal
Abstract
Objectives : There is a need to develop a data quality management algorithm to improve the quality of healthcare data using a data quality management system. In this study, we developed a data quality control algorithms associated with diseases related to hypertension in patients with diabetes mellitus. Methods : To make a data quality algorithm, we extracted the 2011 and 2012 discharge damage survey data from diabetes mellitus patients. Derived variables were created using the primary diagnosis, diagnostic unit, primary surgery and treatment, minor surgery and treatment items. Results : Significant factors in diabetes mellitus patients with hypertension were sex, age, ischemic heart disease, and diagnostic ultrasound of the heart. Depending on the decision tree results, we found four groups with extreme values for diabetes accompanying hypertension patients. Conclusions : There is a need to check the actual data contained in the Outlier (extreme value) groups to improve the quality of the data.
Keywords
Data Mining; Data Quality Management Algorithm; Outlier Detection Method; Diabetes Mellitus; Hypertension;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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1 M.G. Kim, Y.H. Cho, J.H. Park, ETRI(2013), Healthcare Big Data Industry Forecast and competitiveness directions, IT issue report, Vol.27;2-4.
2 T.M. Song(2013), Big data Trends and Utilization of Health & Welfare in Korea, Science and Technology Policy Institute, Vol.23(3);56-73.
3 Medical Record Institute(2002,) HEALTHCARE DOCUMENTATION: A REPORT ON INFORMATION CAPTURE AND REPORT GENERATION, pp.11-14.
4 W.W. Eckerson(2002), Data Quality and the Bottom Line: Achieving Business Success Through a Commitment to High Quality Data. The Data Warehousing Institute Report Series, Chatsworth, USA, pp.11-15.
5 http://www.informationweek.com/healthcare/clinical-information-systems/poor-data-management-costs-healthcare-providers/d/d-id/1105481?
6 Korea Database Agency(2010), 2010 Data quality management maturity level research report, p.13.
7 I.S. Cho(2009), Assessing the Quality of Structured Data Entry for the Secondary Use of Electronic Medical Records, Med Informatics, Vol.15(4);423-431.
8 Juliano(2011), A Systemic Review Of Outlier Detection Techniques In Medical Data: Preminary Data, In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pp.575-782.
9 http://kosis.kr/statHtml/statHtml.do?orgId=350&tblId=DT_35001_A071111&conn_path=I2
10 K. Suganya, S. Dhamodharan(2014), Assessment of Data Quality in healthcare Using Association Rules, International Journal of Engineering and Advanced Technology, Vol.3(4);36-37.
11 S.H. Kang, H.S. Seok, W.J. Kim(2013), The Variation of Factors of severity-adjusted length of stay(LOS) in acute stroke patients. The Journal of Digital Policy & Management, Vol.11(6);221-233.
12 Y.M. Kim(2011), A study on analysis of factors on in-hospital mortality for community-acquired pneumonia, Journal of the Korean Data & Information Science Society, Vol.22(3);389-400.
13 Y.M. Kim, D.G. Cho, S.O. Hong, E.J. Kim, S.H. Kang(2014), Analysis on Geographical Variations of the Prevalence of Hypertension Using Multi-year Data, The Korean Geographical Society, Vol.49(6);935-948.
14 I.S. Park, E.J. Kim, Y.M. Kim, S.O. Hong, Y.T. Kim, S.H. Kang(2015), A Study on Regional Variations for Disease-specific Cardiac Arrest, Journal of Digital Convergence, Vol.13(1);353-366.
15 M. Ankerst, C. Elsen, M. Ester, H.P. Kriegel(1999), Visual Classication: An Interactive Approach to Decision Tree Construction, KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and datamining, pp.392-396.
16 E.H. Jee(2015), Study on Optimization of Customer Satisfaction hospital, Doctoral thesis Health Administration, Inje university Graduate school, p.4.
17 J.H. Lim, S.H. Kang(2015), Convergence-based analysis on geographical variations of the smoking rates, Journal of Digital Convergence, Vol.13(8);375-385.   DOI
18 S.H. Park, B.D. Hwang(2013), The Effect of Their Sense of Depression and Suicidal Thinking for Managerial Characteristics in Hypertense and Diabetic Patients, The Journal of health Service Management, Vol.7(4):221-232.   DOI