• Journal of Society of Preventive Korean Medicine
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• v.23 no.3
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• pp.1-12
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• 2019

Objectives : Korean national health insurance data is a useful real-world data representing whole medical bills submitted to Health Insurance Review Agency. This study aims to understand recent benefit trend of insurance herbal preparations for treating bronchiectasis(disease code J47) utilizing insurance data. Methods : We reviewed national health insurance claims data from 2013 to 2017 which have main diagnosis or sub diagnosis code of J47 and with the record of prescribing insurance herbal medication. Frequency analysis was performed to analyze the most frequently prescribed prescription. Results & Conclusions : Both the number of claims statement(770 to 1,746cases) and patients(266 to 484) of insurance herbal preparations increased considerably from 2013 to 2017. Top 10 preparations based on the number of claims statement were 'Samso-eum', 'Yeonkyopaedok-san', 'Socheongryong-tang', 'Bojungikgi-tang', 'Hyangsapyungwi-san', 'Yijin-tang', 'Saengmaek-san', 'Jaeumganghwa-tang', 'Ojeok-san' and 'Gungha-tang'. Top 10 preparations based on the number of patients were 'Samso-eum', 'Socheongryong-Tang', 'Saengmaek-san', 'Yeonkyopaedok-san', 'Haengso-tang', 'Hyangsapyungwi-san', Yijin-tang', 'Jaeumganghwa-tang', 'Bojungikgi-tang' and 'Hyeonggaeyeongyo-tang' in respectiv order. Claims of top 10 frequent preparations occupied more than 60% of total claims. We hope this finding to be utilized as basic data for future research of evidence-based bronchiectasis treatment utilizing Korean traditional medicine.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.125-131
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• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.665-675
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• 2020

The aim of this article is to investigate the relationship between insurance and economic growth at aggregate and disaggregate level for the period 1982-2018. Very few studies have been carried out in this field, with contradictory results and using an aggregate data while, according to different authors, an aggregate data might provide spurious results. The author used Ordinary Least Squares Regressions (OLS) and Granger Causality tests to explore the strength and direction of the relationship between insurance and economic growth at an aggregate level. To check the relationship at disaggregate level life insurance, marine insurance, and property insurance are regressed on trade openness and investment, respectively. Non-life insurance at an aggregate level plays a positive and significant role in promoting economic growth, but life insurance has an insignificant impact on the Pakistan economy. On the other hand, non-life insurances at a disaggregated level such as marine insurance negatively affect a vital part of economic growth, i.e., trade. At the same time, property insurance has a significant and positive role in boosting investment. Life, marine, and property insurance Granger cause economic growth, trade, and investment in a single direction. Nevertheless, is a bi-directional relationship between economic growth and non-life insurance.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.9-14
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• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.693-706
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• 2018

It is very important to support the elderly with disability ageing in place. Assisting devices can help them to live independently in their community; however, they have to be used appropriately to meet care needs. This study develops an assisting device recommendation system for the beneficiaries of long-term care insurance that include algorithms to decide the most appropriate type of assisting device for beneficiaries. We used long-term care (LTC) insurance data for grade assessment including 8,084 beneficiaries from July 2015 to June 2016. In addition, we collected standard care plans for assisting devices, that power-assessors made, considering their performance and ability that could subsequently be matched with grade assessment data. We used a decision-tree model in data-mining to develop the model. Finally, we developed 15 algorithms for recommending assisting devices. The findings might be useful in evidence-based care planning for assisting devices and can contribute to enhancing independence and safety in LTC.

• Korea Journal of Hospital Management
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• v.28 no.2
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• pp.9-20
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• 2023

Purpose: This study classified the actual functions of geriatric hospitals and examined the differences in their characteristics, in order to provide a basis for discussions on defining the functions of geriatric hospitals and how to pay for care. Methodology: This study used various administrative data such as health insurance data and long-term care insurance data. Cluster analysis was used to categorize geriatric hospitals. To examine the validity of the cluster analysis results, we conducted a discriminant analysis to calculate the accuracy of the classification. To examine cluster characteristics, we examined structure, process, and outcome indicators for each cluster. Findings: The cluster analysis identified five clusters. They were geriatric hospitals with relatively short stays for cancer patients(cluster 1; cancer patient-centered), geriatric hospitals with relatively large numbers of patients using rehabilitation services(cluster 2; rehabilitation patient-centered), geriatric hospitals with a high proportion of relatively severe elderly patients(cluster 3; severe elderly patient-centered), geriatric hospitals with a high proportion of mildly ill elderly patients with various conditions(cluster 4; mildly ill elderly patient-centered), and geriatric hospitals with a significantly higher proportion of dementia patients(cluster 5; dementia patient-centered). The largest number of geriatric hospitals were categorized in clusters 4 and 5, and the structure and process indicators for these clusters were generally lower than for the other clusters. Practical Implications: We have confirmed the existence of geriatric hospitals where the medical function, which is the original purpose of a geriatric hospital, has been weakened. It has been observed that the quality level of these geriatric hospitals is likely to be lower compared to hospitals that prioritize enhanced medical functions. Therefore, it is suggested to consider the conversion of these geriatric hospitals into long-term care facilities, and careful consideration should be given to the review of care-giver payment coverage.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.163-170
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• 2009

The purpose of this article is to measure the level of insurance industry's social cognition based on the university students' survey questionnaire. For this, this study analyzes the cognition of students on the level of social reliability, the causes of low reliability, and the measures of enhancing the reliability level about insurance industry. For the analysis of survey questionnaire, partially ranked data analysis was employed. The results show that most students recognize that the reliability of insurance industry is lower than that of other financial industry such as banks and securities firms. The main reason for this low reliability is the lack of sales forces' specialty, the complexity of insurance products and contracts, and negative media release about insurance industry. To enhance the social reliability level of insurance, establishment of the future insurance image is essential, and that can be achieved through strengthening the insurance education and public relations and simplifying insurance products and contracts.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.10 no.3
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• pp.300-306
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• 2003

Purpose: This study was done to help provide patients with information on medical insurance cost through medical insurance education for nurses, to increase effective management, check on omissions in treatment and appropriateness and accuracy of fees, and to contribute to the economic growth of hospital by providing nurses with necessary knowledge about medical insurance cost. Method: The participants in this study were clinical nurses in general hospitals. The study instrument was a questionnaire developed by the researcher through reference to data for medical insurance education. The data were analyzed with percentages, means, ANOVA, and Duncan method using SPSS PC+10. Result: The results on knowledge of medical insurance according to general characteristics of the nurses showed that there were significant differences according to age: (p=.0036) highest level of education (p=.0007), position (p=.0010) and place where education on medical insurance was received (p=.0093). Conclusion: Continuous in-service education for clinical nurses is reflected in increased knowledge about medical insurance costs but special attention needs to be given to younger nurses and nurses with less education, as well as staff nurses, and those nurses who only received education on medical insurance during their schooling. Accordingly, in-service education is necessary for nurses at the time of orientation so that they have knowledge on standards for recuperation allowance, guidelines to calculate material costs, and guidelines to calculate drug rates. In addition, as medical insurance cost frequently change, all nurses need continuous in-service education.

• Journal of Preventive Medicine and Public Health
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• v.54 no.1
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• pp.8-16
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• 2021

This article aims to introduce the inception and operation of the COVID-19 International Collaborative Research Project, the world's first coronavirus disease 2019 (COVID-19) open data project for research, along with its dataset and research method, and to discuss relevant considerations for collaborative research using nationwide real-world data (RWD). COVID-19 has spread across the world since early 2020, becoming a serious global health threat to life, safety, and social and economic activities. However, insufficient RWD from patients was available to help clinicians efficiently diagnose and treat patients with COVID-19, or to provide necessary information to the government for policy-making. Countries that saw a rapid surge of infections had to focus on leveraging medical professionals to treat patients, and the circumstances made it even more difficult to promptly use COVID-19 RWD. Against this backdrop, the Health Insurance Review and Assessment Service (HIRA) of Korea decided to open its COVID-19 RWD collected through Korea's universal health insurance program, under the title of the COVID-19 International Collaborative Research Project. The dataset, consisting of 476 508 claim statements from 234 427 patients (7590 confirmed cases) and 18 691 318 claim statements of the same patients for the previous 3 years, was established and hosted on HIRA's in-house server. Researchers who applied to participate in the project uploaded analysis code on the platform prepared by HIRA, and HIRA conducted the analysis and provided outcome values. As of November 2020, analyses have been completed for 129 research projects, which have been published or are in the process of being published in prestigious journals.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.16 no.2
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• pp.137-149
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• 2012

We propose an efficient method to measure the insurance risk of causality insurance companies by using the CreditRisk+ methodology. This method is superior to previous methods in several aspects. Its computation speed is very fast and the input data form is simple. It is able to aggregate both credit risk and insurance risk, so the insurance company can manage the risk in combined manner. In this paper, we propose a mathematical method to obtain the aggregate loss distribution of portfolios having correlation among products or business lines as a general case, and then suggest its implementation algorithm. Finally we apply this method to the real data from Korea Insurance Development Institute (KIDI) and discuss its availability to real applications.