1.Methodology for Big Data Analysis Using Data from National Health Insurance Service: Preliminary Methodologic Study and Review about the Relationship between Sinus Surgery and Asthma.
Seunghak YU ; Jaewoon WEE ; Jeong Whun KIM ; Sungroh YOON
Journal of Rhinology 2015;22(1):28-33
BACKGROUND AND OBJECTIVES: Sinus surgery has been reported to improve pulmonary function and decrease the use of asthma medications in patients with chronic rhinosinusitis and asthma. Most studies, however, have used a small number of patients and were conducted over a short period. To demonstrate a causal relationship between sinus surgery and asthma, a sufficient amount of patient data observed over a long period is required. To address the limitations of the existing approaches, we conducted a preliminary methodological study for large-scale data analysis using data from the National Health Insurance Service (NHIS) to suggest a basis for the effect of sinus surgery on asthma. MATERIALS AND METHODS: The data from NHIS consisted of unidentified medical histories of a sample cohort representing the whole nation over a period of nine years. We selected the following types of study samples: 1) patients with surgical codes for sinus surgery; 2) patients with disease codes for sinusitis; 3) patients with disease codes for asthma; and 4) patients with medication codes for asthma treatment. RESULTS: In this study, we applied a methodology for selection of subjects from big data to investigate the effect of sinus surgery on improving asthma in the future. We could include 152 subjects after the four-stage selection method from 1,025,340 patients. CONCLUSION: We could establish a method to select patients with history of sinus surgery and asthma treatment from a big data. This methodology using big data may contribute to identify relationship between sinus surgery and asthma treatment in the future.
Asthma*
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Cohort Studies
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Humans
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Methods
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National Health Programs*
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Patient Selection
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Sinusitis
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Statistics as Topic*
2.Refinement and Evaluation of Korean Outpatient Groups for Visits with Multiple Procedures and Chemotherapy, and Medical Visit Indicators.
Hayoung PARK ; Gil Won KANG ; Sungroh YOON ; Eun Ju PARK ; Sungwoon CHOI ; Seunghak YU ; Eun Ju YANG
Health Policy and Management 2015;25(3):185-196
BACKGROUND: Issues concerning with the classification accuracy of Korean Outpatient Groups (KOPGs) have been raised by providers and researchers. The KOPG is an outpatient classification system used to measure casemix of outpatient visits and to adjust provider risk in charges by the Health Insurance Review & Assessment Service in managing insurance payments. The objective of this study were to refine KOPGs to improve the classification accuracy and to evaluate the refinement. METHODS: We refined the rules used to classify visits with multiple procedures, newly defined chemotherapy drug groups, and modified the medical visit indicators through reviews of other classification systems, data analyses, and consultations with experts. We assessed the improvement by measuring % of variation in case charges reduced by KOPGs and the refined system, Enhanced KOPGs (EKOPGs). We used claims data submitted by providers to the HIRA during the year 2012 in both refinement and evaluation. RESULTS: EKOPGs explicitly allowed additional payments for multiple procedures with exceptions of packaging of routine ancillary services and consolidation of related significant procedures, and discounts ranging from 30% to 70% were defined in additional payments. Thirteen chemotherapy drug KOPGs were added and medical visit indicators were streamlined to include codes for consultation fees for outpatient visits. The % of variance reduction achieved by EKOPGs was 48% for all patients whereas the figure was 40% for KOPGs, and the improvement was larger in data from tertiary and general hospitals than in data from clinics. CONCLUSION: A significant improvement in the performance of the KOPG was achieved by refining payments for visits with multiple procedures, defining groups for visits with chemotherapy, and revising medical visit indicators.
Classification
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Drug Therapy*
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Fee-for-Service Plans
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Fees and Charges
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Health Care Costs
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Hospitals, General
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Humans
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Information Systems
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Insurance
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Insurance Claim Review
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Insurance, Health
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Outpatients*
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Product Packaging
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Prospective Payment System
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Referral and Consultation