scholarly journals Breast reconstruction statistics in Korea from the Big Data Hub of the Health Insurance Review and Assessment Service

2018 ◽  
Vol 45 (5) ◽  
pp. 441-448 ◽  
Author(s):  
Jae-Won Kim ◽  
Jun-Ho Lee ◽  
Tae-Gon Kim ◽  
Yong-Ha Kim ◽  
Kyu Jin Chung
Keyword(s):  
Big Data ◽  
Health Insurance ◽  
Breast Reconstruction

scholarly journals Current status of and trends in post-mastectomy breast reconstruction in Korea

2020 ◽  
Vol 47 (2) ◽  
pp. 118-125 ◽  
Author(s):  
Woo Jin Song ◽  
Sang Gue Kang ◽  
Eun Key Kim ◽  
Seung Yong Song ◽  
Joon Seok Lee ◽  
...  
Keyword(s):  
Big Data ◽  
Health Insurance ◽  
Breast Reconstruction ◽  
Tissue Expander ◽  
Perforator Flap ◽  
Free Tissue Transfer ◽  
Current Status ◽  
Autologous Reconstruction ◽  
Annual Changes ◽  
Reconstruction Rate

Since April 2015, post-mastectomy breast reconstruction has been covered by the Korean National Health Insurance Service (NHIS). The frequency of these procedures has increased very rapidly. We analyzed data obtained from the Big Data Hub of the Health Insurance Review and Assessment Service (HIRA) and determined annual changes in the number of breast reconstruction procedures and related trends in Korea. We evaluated the numbers of mastectomy and breast reconstruction procedures performed between April 2015 and December 2018 using data from the HIRA Big Data Hub. We determined annual changes in the numbers of total, autologous, and implant breast reconstructions after NHIS coverage commenced. Data were analyzed using Microsoft Excel. The post-mastectomy breast reconstruction rate increased from 19.4% in 2015 to 53.4% in 2018. In 2015, implant reconstruction was performed in 1,366 cases and autologous reconstruction in 905 (60.1% and 39.8%, respectively); these figures increased to 3,703 and 1,570 (70.2% and 29.7%, respectively) in 2018. Free tissue transfer and deep inferior epigastric perforator flap creation were the most common autologous reconstruction procedures. For implant-based reconstructions, the rates of directto-implant and tissue-expander breast reconstructions (first stage) were similar in 2018. This study summarizes breast reconstruction trends in Korea after NHIS coverage was expanded in 2015. A significant increase over time in the post-mastectomy breast reconstruction rate was evident, with a trend toward implant-based reconstruction. Analysis of data from the HIRA Big Data Hub can be used to predict breast reconstruction trends and convey precise information to patients and physicians.

scholarly journals Analysis of Health Insurance Big Data for Early Detection of Disabilities: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Seung-Hyun Jeong ◽  
Tae Rim Lee ◽  
Jung Bae Kang ◽  
Mun-Taek Choi
Keyword(s):  
Big Data ◽  
Health Insurance ◽  
Early Detection ◽  
Developmental Delays ◽  
Brain Lesion ◽  
Detection Model ◽  
Clinical Diagnoses ◽  
Significant Performance ◽  
Clinically Significant ◽  
Age Range

BACKGROUND Early detection of childhood developmental delays is very important for the treatment of disabilities. OBJECTIVE To investigate the possibility of detecting childhood developmental delays leading to disabilities before clinical registration by analyzing big data from a health insurance database. METHODS In this study, the data from children, individuals aged up to 13 years (n=2412), from the Sample Cohort 2.0 DB of the Korea National Health Insurance Service were organized by age range. Using 6 categories (having no disability, having a physical disability, having a brain lesion, having a visual impairment, having a hearing impairment, and having other conditions), features were selected in the order of importance with a tree-based model. We used multiple classification algorithms to find the best model for each age range. The earliest age range with clinically significant performance showed the age at which conditions can be detected early. RESULTS The disability detection model showed that it was possible to detect disabilities with significant accuracy even at the age of 4 years, about a year earlier than the mean diagnostic age of 4.99 years. CONCLUSIONS Using big data analysis, we discovered the possibility of detecting disabilities earlier than clinical diagnoses, which would allow us to take appropriate action to prevent disabilities.

scholarly journals Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics

10.2196/19540 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e19540 ◽  
Author(s):  
Chi-Mai Chen ◽  
Hong-Wei Jyan ◽  
Shih-Chieh Chien ◽  
Hsiao-Hsuan Jen ◽  
Chen-Yang Hsu ◽  
...  
Keyword(s):  
Big Data ◽  
Health Insurance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Case Fatality ◽  
Sensor Data ◽  
Contact Tracing ◽  
Cruise Ship ◽  
Health Insurance Data

Background Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation. Objective We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020. Methods The smart contact tracing–based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2. Results As of February 29, a total of 67 contacts who were tested by reverse transcription–polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020. Conclusions Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing.

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