• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1667-1676
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• 2006

다른 산업과 달리 병원/의료 산업에서는 건강 보험료 심사 평가라는 독특한 검증 과정이 필수적으로 있게 된다. 건강 보험료 심사 평가는 병원의 수익 문제 뿐 아니라 적정한 진료행위를 하는 병원이라는 이미지와도 맞물려 매우 중요한 분야이며, 특히 대형 종합병원일수록 이 부분에 많은 심사관련 인력들을 투입하여, 병원의 수익과 명예를 위해서 업무를 수행하고 있다. 본 논문은 이러한 건강보험료 청구 심사 과정에서, 사전에 수많은 진료 청구 건 중 심사 평가에서 삭감이 될 수 있는 진료 청구 건을 데이터 마이닝을 통해서 발견하여, 사전의 대비를 철저히 하고자 하는 한 국내의 대형 종합병원의 사례를 소개하고자 한다. 데이터 마이닝을 적용함에 있어, 주요한 문제점 중의 하나는 바로 지도학습 기법을 적용하기에 곤란한 데이터 불균형 문제가 발생하는 것이다. 이런 불균형 문제를 해소하고, 비교 조건 중에 가장 효율적인 삭감 예상 진료 건 탐지 모형을 만들어 내기 위하여 데이터 불균형 문제의 기본 해법인 과, Sampling 오분류 비용의 다양하고 혼합적인 적용을 통하여, 적합한 조건을 가지는 의사결정 나무 모형을 도출하였다.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.289-299
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• 2013

The aim of this study was to enhance the NHI claims data-based tuberculosis classification rule of KCDC(Korea centers for disease control & prevention) for an effective TB surveillance system. 8,118 cases, 10% samples of 81,199 TB cases from NHI claims data during 2009, were subject to the Medical Record Survey about whether they are real TB patients. The final study population was 7,132 cases whose medical records were surveyed. The decision tree model was evaluated as the most superior TB patients detection model. This model required the main independent variables of age, the number of anti-tuberculosis drugs, types of medical institution, tuberculosis tests, prescription days, types of TB. This model had sensitivity of 90.6%, PPV of 96.1%, and correct classification rate of 93.8%, which was better than KCDC's TB detection model with two or more NHI claims for TB and TB drugs(sensitivity of 82.6%, PPV of 95%, and correct classification rate of 80%).

• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.195-200
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• 2019

The purpose of this study is to analyze the characteristics of National Health Insurance claim data and to construct a pilot medical episode data considering it. In this study, the trends of respiratory disease (ICD10: J00-J99) cardiovascular disease (ICD10: I00-I99) from the day of onset of treatment to re-admission after admission were confirmed in Seoul, and the largest decrease was observed when the no-treatment period was 0 day. The data reduction rate when the no-treatment period is 0 day is judged to be due to the monthly separation claim of the health insurance claim data. Also, the result that there is a tendency of monthly separation request according to the type of medical treatment. Through this study, we constructed epidemic data for the pilot medical treatment considering the characteristics of the claim data of health insurance, and based on this, it can be used as a data processing method for calculating basic epidemiological information.

• Information Systems Review
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• v.9 no.1
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• pp.45-65
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• 2007

In medical industry, health insurance bill audit is unique and essential process in general hospitals. The health insurance bill audit process is very important because not only for hospital's profit but also hospital's reputation. Particularly, at the large general hospitals many related workers including analysts, nurses, and etc. have engaged in the health insurance bill audit process. This paper introduces a case of health insurance bill audit for finding reducible health insurance bill cases using decision tree induction techniques at a large general hospital in Korea. When supervised learning methods had been tried to be applied, one of major problems was data imbalance problem in the health insurance bill audit data. In other words, there were many normal(passing) cases and relatively small number of reduction cases in a bill audit dataset. To resolve the problem, in this study, well-known methods for imbalanced data sets including over sampling of rare cases, under sampling of major cases, and adjusting the misclassification cost are combined in several ways to find appropriate decision trees that satisfy required conditions in health insurance bill audit situation.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.467-476
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• 2011

We developed the hybrid model coupled with predictive model and business rule model for administration of injury by utilizing medical data of the National Health Insurance in Korea. We performed decision tree analysis using data mining methodology and used SAS Enterprise Miner 4.1. We also investigated under several business rule for benefits (expense paid by insurer) and claims of injury in National Health Insurance Corporation. We can see that the proposed hybrid model provides a quite efficient plausible results.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.1-9
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• 2023

Research using AI and big data is also being actively conducted in the health and medical fields such as disease diagnosis and treatment. Most of the existing research data used cohort data from research institutes or some patient data. In this paper, the difference in the prediction rate of survival and the factors affecting survival between breast cancer patients in their 40~50s and other age groups was revealed using health insurance review claim data held by the HIRA. As a result, the accuracy of predicting patients' survival was 0.93 on average in their 40~50s, higher than 0.86 in their 60~80s. In terms of that factor, the number of treatments was high for those in their 40~50s, and age was high for those in their 60~80s. Performance comparison with previous studies, the average precision was 0.90, which was higher than 0.81 of the existing paper. As a result of performance comparison by applied algorithm, the overall average precision of Decision Tree, Random Forest, and Gradient Boosting was 0.90, and the recall was 1.0, and the precision of multi-layer perceptrons was 0.89, and the recall was 1.0. I hope that more research will be conducted using machine learning automation(Auto ML) tools for non-professionals to enhance the use of the value for health insurance review claim data held by the HIRA.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.265-275
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• 2020

The burden of injury is widely considered to be very severe in our society. Nonetheless, we don't have enough data for injury surveillance. The objective of this research is to select and supplement CORE DATA for injury surveillance. For this purpose, this study had analyzed the literature such as the Quality Assessment Report about 'Causes of Death Statistics', 'Health Insurance Statistics' and 'Hospital Discharge Injury Surveillance' according to the six dimension of Statistics Quality. The analysis result is that 'Cause of Death Statistics' and 'Health Insurance Statistics' have the usefulness as the CORE DATA for injury surveillance. But there is a significant shortcoming in the Health Insurance Statistics, which is that there is a lack of the data about the external causes of injury. For supplementing the defect, this study proposes the system that the medical institutions should obligatorily report the external causes of injury when claim National Health Insurance Medical Care Expenses. As the results of this system, we can expect 'Establishing of Injury pyramid', 'Data Connecting with the National Pension' and 'Improving the Promptness of Injury Data'. And we expect the follow-up study for the realization of this system.

• Journal of Convergence for Information Technology
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• v.8 no.4
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• pp.9-14
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• 2018

The purpose of this study was to analyse the relationship between the characteristics of the patients who received oral antihyperglycemic drugs and their medical adherence in Korea. The study method was a cross-sectional study using the patient sample data of the Health Insurance Review and Assessment Service for 2016, and it was analyzed with 109 major components of diabetes drug. The medical adherence was slightly higher in male than female. The patriots & veterans(free) type had the highest medication adherence because they have low self burden to access medical institutions compared to other insurance types. It is expect that this study result will be used as a basic data to understand the burden of outpatients with health insurance and establish a policy to reduce of the self outpatients' burden with chronic diseases such as type 2 diabetes.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.593-608
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• 2013

According to increasing number of injury claims, the challenge is reducing investigation of cases of injuries by selecting them more delicately, while also increasing the redemption rates and the amount of restitution. In this regards, we developed the fraud detection model for injury claims of self-employed insured by using decision tree after collecting medical claim data from 2006 to 2011 of the National Health Insurance in Korea. As a result of this model, subject types were classified into 18 types. If applying these types to the actual survey compared with if not applying, the redumption collecting rate will be increasing by 12.8%. Also, the effectiveness of this model will be maximize when the number of claims handlers considering their survey volume and management plans are examined thoroughly.

• Korean Journal of Clinical Pharmacy
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• v.21 no.2
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• pp.156-160
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• 2011

서론: 본 연구는 불필요한 소화기관용 약제의 처방이 한국에서의 처방전 당 약물 개수를 증가시키는 것과의 연관성을 검토하고자 수행되었다. 연구방법: 연구를 위한 자료로 건강보험심사평가원의 처방전 데이터와 환자의 기타 모든 의료보험 청구데이터를 이용하였고, 두 데이터셋을 연결하여 처방전들을 소화기관용 약제의 필요성에 따라 소화기관질환 그룹, 관절염질환 그룹,소화기관용 약제 처방이 불필요할 것으로 그 외 질환 그룹으로 구분, 분리하였다. 결과: 처방전 당 약물의 평균 개수의 분포는 세 그룹에서 비슷한 양상을 보였는데, 관절염질환 그룹과 그 외 질환 그룹의 거의 절반 이상은 한 개의 소화기관용 약제를 포함하였다. 세 그룹 모두 처방전 당 약물 개수와 처방전 당소화기관용 약제의 개수가 1차 선형관계를 보였다. 그 외 질환 그룹에서는 처방전 당 전체 약물이 평균 6개를 넘는 경우, 적어도 한 개의 소화기관용 약제가 포함되었다. 본 연구는 불필요한 소화기관용 약제를 처방하는 것은 다제처방의 매우 유의한 예측인자임을 보였다. 결론: 향후, 약제 처방전의 질을 향상시키기 위해서는 각각의 약물을, 특히 소화기관용 약제를, 처방 시 약제의 불가피한 필요성에 대해 판단할 수 있어야 할 것이다.