The Journal of Korean Medicine (대한한의학회지)

The Society of Korean Medicine (대한한의학회)
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Study on the distribution in major disease category and frequency of clinical usage of national health insurance herbal prescription based on analysis on KCD8 disease code of indications

적응증의 KCD8 상병코드 분석 결과를 기반으로 한 보험한약제제의 질병 대분류 분포 분석과 사용 빈도 연구

  • Dong Woo Lim (Department of Diagnostics, College of Korean Medicine, Dongguk University) ;
  • Jung Yun Ahn (Dasram Cancer Care Hospital) ;
  • Ga Ram Yu (Department of Diagnostics, College of Korean Medicine, Dongguk University) ;
  • Jai Eun Kim (Department of Pathology, College of Korean Medicine, Dongguk University) ;
  • Won Hwan Park (Department of Diagnostics, College of Korean Medicine, Dongguk University)
  • 임동우 (동국대학교 한의과대학 진단학교실) ;
  • 안정윤 (다스람 요양병원) ;
  • 유가람 (동국대학교 한의과대학 진단학교실) ;
  • 김재은 (동국대학교 한의과대학 병리학교실) ;
  • 박원환 (동국대학교 한의과대학 진단학교실)
  • Received : 2022.08.01
  • Accepted : 2023.02.14
  • Published : 2023.03.01
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Abstract

Objectives: National health insurance herbal prescription of Korean medicine has been serving important role in public healthcare in spite of continuous demand on revision of system. However, the categories of insurance herbal prescriptions are not equally distributed throughout the KCD-based major disease categories. We analyzed statistical database of claimed national health insurance classified as major disease categories by years. We classified all 56 herbal prescriptions as per their total medical indications into 22 major disease categories to analyze their distribution. Significant increase of M and S-T code claims were found, whereas decrease of U code claims by years. We figured out that the 56 prescriptions were unequally distributed along with enrichment of certain codes such as K and J. Meanwhile, the insurance claim of each prescription was positively correlated with number of code types of their indications. As a result, we believe that the reform of national health insurance herbal prescription list is necessary to promote use of it in clinic.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. NRF-2022R1A2C1010265).

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