Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Trend Analysis of Fraudulent Claims by Long Term Care Institutions for the Elderly using Text Mining and BIGKinds

텍스트 마이닝과 빅카인즈를 활용한 노인장기요양기관 부당청구 동향 분석

  • Youn, Ki-Hyok (Department of Social Welfare, Tongmyong University)
  • 윤기혁 (동명대학교 사회복지학과)
  • Received : 2022.02.21
  • Accepted : 2022.03.26
  • Published : 2022.04.30
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Abstract

In order to explore the context of fraudulent claims and the measures for preventing them targeting the long-term care institutions for the elderly, which is increasing every year in Korea, this study conducted the text mining analysis using the media report articles. The media report articles were collected from the news big data analysis system called 'BIG KINDS' for about 15 years from July 2008 when the Long-Term Care Insurance for the Elderly took effect, to February 28th 2022. During this period of time, total 2,627 articles were collected under keywords like 'elderly care+fraudulent claims' and 'long-term care+fraudulent claims', and among them, total 946 articles were selected after excluding overlapped articles. In the results of the text mining analysis in this study, first, the top 10 keywords mentioned in the highest frequency in every section(July 1st 2008-February 28th 2022) were shown in the order of long-term care institution for the elderly, fraudulent claims, National Health Insurance Service, Long-Term Care Insurance for the Elderly, long-term care benefits(expenses), elderly care facilities, The Ministry of Health & Welfare, the elderly, report, and reward(payment). Second, in the results of the N-gram analysis, they were shown in the order of long-term care benefits(expenses) and fraudulent claims, fraudulent claims and long-care institution for the elderly, falsehood and fraudulent claims, report and reward(payment), and long-term care institution for the elderly and report. Third, the analysis of TF-IDF was similar to the results of the frequency analysis while the rankings of report, reward(payment), and increase moved up. Based on such results of the analysis above, this study presented the future direction for the prevention of fraudulent claims of long-term care institutions for the elderly.

본 연구는 우리나라에서 매년 증가하고 있는 노인장기요양기관의 부당청구 맥락과 부당청구 예방을 위한 대책들이 어떠한지를 탐색하기 위해서 언론기사를 활용한 텍스트 마이닝 분석을 실시하였다. 기사는 뉴스 빅테이터 분석 시스템인 빅카인즈에서 수집하였고, 수집기간은 노인장기요양보험이 시행된 2008년 7월부터 2022년 2월 28일까지로 약 15년간이다. 이 기간 동안 '노인요양+부당청구', '장기요양+부당청구', 등의 키워드로 총 2,627개의 기사가 수집되었고, 이중 중복된 기사를 제외한 총 946개가 선정되었다. 본 연구의 텍스트마이닝 분석결과로 첫째, 모든 구간(2008.7.1-2022.2.28)에서 가장 높은 빈도로 언급된 상위 10위 키워드는 노인장기요양기관, 부당청구, 국민건강보험공단, 노인장기요양보험, 장기요양급여(비용), 노인요양시설, 보건복지부, 노인, 신고, 포상금(지급)의 순으로 나타났다. 둘째, N-gram 분석결과 장기요양급여(비용)과 부당청구, 부당청구와 노인장기요양기관, 허위와 부당청구, 신고와 포상금(지급), 노인장기요양기관과 신고 등의 순으로 나타났다. 셋째, TF-IDF 분석은 빈도분석의 결과와 유사하게 나타났지만, 신고, 포상금(지급), 증가 등은 순위가 상승하였다. 상기 분석결과를 바탕으로 노인장기요양기관 부당청구 예방을 위한 방향성을 제시하였다.

Keywords

Acknowledgement

본 논문은 2021년 대한민국 교육부와 한국연구재단의 인문사회분야 신진연구자지원사업의 지원을 받아 수행된 연구임 (NRF-2021S1A 5A8063251)

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