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

Abstract

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.