Hyejung Chang

Measuring the Burden of Disease in Korea

This paper provides an overview of the Korean Burden of Disease (KBoD) study, which was the first such study to assess the national burden of disease using disability-adjusted life years (DALYs) in an advanced Asian country. The KBoD study generally followed the approach utilized in the original Global Burden of Disease study (GBD), with the exception of the disease classification and epidemiological data estimation methods used, and the relative weightings of disabilities. The results of the present study reveal that the burden of disease per 100,000 of the Korean population originates primarily from; cancer (1,525 Person Years, PYs), cardiovascular disease (1,492 PYs), digestive disease (1,140 PYs), diabetes mellitus (990 PYs), and certain neuro-psychiatric conditions (883 PYs). These results are largely consistent with those of developed countries, but also represent uniquely Korean characteristics.

Interactive data visualization based on conventional statistical findings for antihypertensive prescriptions using National Health Insurance claims data

BACKGROUND Interactive visualization is an important approach to help to understand and to explain large amounts of data, particularly in light of decision support. Although data visualization have been introduced in healthcare and clinical fields, analytics has often been performed by data experts, focused on specific subjects, or insufficient statistical evidence. Therefore, this study suggests the procedures of effective and efficient visualization of big data for general healthcare researchers. Specifically, the procedure includes conventional regression analyses followed by interactive data visualization for prescription patterns of antihypertensive drugs. METHODS As a large-scale nationally representative prescription data, the Korean National Health Insurance claims data were collected. Conventional descriptive and regression analyses were conducted for therapy decision and prescription patterns using the software R. Then, based on the statistically significant findings, dashboards were developed to visualize interactively the patterns of prescriptions using the software Tableau. RESULTS Major characteristics (genders, age groups, healthcare institutions, and comorbidities) explained the differences in therapy and the average number of drugs prescribed as well as differences among most commonly prescribed drug classes. Two interactive dashboards were created for visualizing prescription patterns with incorporation of horizontal bar charts, packed bubble charts, treemaps, filled maps, radar charts, box and whisker plots, and filters. CONCLUSION In the current big data era, interactive data visualization offers substantial opportunities to have comprehensive view, extract insights and evidence from the flood of vast amounts of data. This studys interactive visualizations can provide healthcare professionals insight into prescription patterns and demonstrate the value of creating interactive dashboards to support informed and timely decision-making. Exploring big data using interactive visualization is expected to deliver many future benefits in healthcare fields.

Characteristics of Hospital by Network Type in Korea

With the competitive environment accelerating in healthcare industry, the hospital network system is considered as one of the strategies for clinical and managerial efficiency. This study was intended to offer a theoretical view on the hospital network system and to analyze the current network status of hospitals in Korea. Specifically, network types were classified based on the criteria modified from previous studies, and were used to describe and compare the scope and intensity of associated activities. The questionnaire survey was conducted with 237 hospitals during the period of December 27 2005 to January 25 2006. Above 90% of tertiary and secondary care hospitals were under the network system, while only 20% of primary care clinics were affiliated. In general, the scope and intensity of network activities was limited. Vertical and/or clinical integration was more common than horizontal and/or managerial integration. Three most frequent types of hospital network systems were clinical-vertical integration (Type A), clinical/managerial-vertical integration(Type B), and clinical/managerial-horizontal /vertical integration (Type C). Such network types differentiated significantly different features of affiliated hospitals and network systems. The affiliation duration to the network system was the only significant factor influencing on the network type. The strategic approach to the network system was emphasized for hospitals to increase the potential advantage of hospital network systems.