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퇴원손상심층조사 자료를 기반으로 한 급성심근경색환자 재원일수의 중증도 보정 모형 개발

Severity-Adjusted LOS Model of AMI patients based on the Korean National Hospital Discharge in-depth Injury Survey Data

  • Kim, Won-Joong (Division of Health Policy and Management, Inje University) ;
  • Kim, Sung-Soo (Division of Political Science and International Relations, Inje University) ;
  • Kim, Eun-Ju (Division of Health Policy and Management, Inje University) ;
  • Kang, Sung-Hong (Division of Health Policy and Management, Inje University)
  • 투고 : 2013.07.23
  • 심사 : 2013.10.10
  • 발행 : 2013.10.31

초록

본 연구는 급성심근경색환자의 효율적인 재원일수 관리를 위해 재원일수에 대한 중증도 보정 모형을 개발하고자 하였다. 2004-2009년 퇴원손상심층조사 자료에서 주진단이 I21인 급성심근경색환자 6,074명을 추출하였으며, 모형 개발 시 데이터마이닝 기법(다중회귀분석, 의사결정나무, 신경망 기법)을 적용하였다. 개발된 모형들 중에서 의사결정나무 모형이 가장 우수한 모형으로 판정되어 이를 본 연구의 중증도 보정 모형으로 채택하였다. 급성심근경색 환자의 재원일수의 중증도 보정에 영향을 미치는 주요한 요인은 관상동맥우회술 시행유무, 퇴원 시 사망유무, 동반지수 등 이였으며, 병상규모와 의료기관 소재지 별로 중증도 보정 재원일수와 실제 재원일수에 차이가 있었다. 급성심근경색환자의 재원일수 변이를 줄이고 효율적으로 관리하기 위해서는 개발된 모형에 각 의료기관의 자료를 적용하여 중증도를 보정한 후, 차이가 나는 요인을 규명하여 이를 해결하는 활동이 수행되어야 할 것이다.

This study aims to design a Severity-Adjusted LOS(Length of Stay) Model in order to efficiently manage LOS of AMI(Acute Myocardial Infarction) patients. We designed a Severity-Adjusted LOS Model with using data-mining methods(multiple regression analysis, decision trees, and neural network) which covered 6,074 AMI patients who showed the diagnosis of I21 from 2004-2009 Korean National Hospital Discharge in-depth Injury Survey. A decision tree model was chosen for the final model that produced superior results. This study discovered that the execution of CABG, status at discharge(alive or dead), comorbidity index, etc. were major factors affecting a Sevirity-Adjustment of LOS of AMI patients. The difference between real LOS and adjusted LOS resulted from hospital location and bed size. The efficient management of LOS of AMI patients requires that we need to perform various activities after identifying differentiating factors. These factors can be specified by applying each hospital's data into this newly designed Severity-Adjusted LOS Model.

키워드

참고문헌

  1. Ministry of Health & Welfare, Yonsei Institute of Health and Welfare, Korean National Health Accounts and Total Health Expenditure in 2010, pp.1-350, Ministry of Health & Welfare, 2012.
  2. Ministry of Health & Welfare, Korea Institute for Health and Social Affairs, OECD Health Data 2012, pp.1-121, KyungSung Publishers, 2012
  3. The Leapfrog Group, Development of Severity-Adjustment Models for Hospital Efficiency Data, pp.1-84, The Center for Health Systems Research and Analysis University of Wisconsin-Madison, 2008.
  4. Ben-Tovim D., Woodman R., Harrison JE., Pointer S., Hakendorf P., Henley G., Measuring and reporting mortality in hospital patients, pp.1-136, Australian Institute of Health and Welfare, 2009.
  5. S. S. Kim, W. J. Kim, S. H. Kang, "A study on the variation of severity adjusted LOS on injury inpatient in Korea", The Korea Academia-Industrial Cooperation Society, Vol.12, No.6, pp.2668-2676, 2011. DOI: http://dx.doi.org/10.5762/KAIS.2011.12.6.2668
  6. Y. M. Kim, Y. K. Choe, S. H. Kang, W. J. Kim, "A study on analysis of severity-adjustment length of stay in hospital for community-acquired pneumonia", The Korea Academia-Industrial Cooperation Society, Vol.12, No.3, pp.1234-1243, 2011. DOI: http://dx.doi.org/10.5762/KAIS.2011.12.3.1234
  7. S. J. Kim, S. H. Kang, W. J. Kim, Y. M. Kim, "The variation factors of severity-adjusted length of stay in CABG", The Korean Society for Quality Management, Vol.39, No.3, pp.391-399, 2011.
  8. OECD/Korea Policy Centre, Health at a Glance 2011(Korean edition), pp.1-199, OECD/Korea Policy Centre, 2012.
  9. Health Insurance Review & Assessment Service, http://www.hira.or.kr
  10. J. H. Lim, J. Y. Park, "The impact of comorbidity (the Charlson Comorbidity Index) on the health outcomes of patients with the acute myocardial infarction(AMI)", Korean Journal of Health Policy and Administration, Vol.21, No.4, pp.541-564, 2011. DOI: http://dx.doi.org/10.4332/KJHPA.2011.21.4.541
  11. The Leapfrog Group, Resource Utilization Measures- Detailed Scoring Algorithm, pp.1-4, The Leapfrog Group, 2013.
  12. Shoemaker W., "Benchmarking boon: tapping publicly available data to improve performance", Healthcare Financial Management Association, Vol. 65, No.6, pp.88-94, 2011.
  13. H. S. Choe, J. H. Lim, W. J. Kim, S. H. Kang, "The effective management of length of stay for patients with acute myocardial infarction in the era of digital hospital", The Korea Society of Digital Policy and Management, Vol.10, No.1, pp.413-422, 2012.
  14. Ministry of Health & Welfare, Korea Centers for Disease Control & Prevention, http://injury.cdc.go.kr
  15. Li B., Evans D., Faris P., Dean S., Quan H., "Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases", BMC Health Services Research, pp.8-12, 2008.
  16. Elixhauser A., Steiner C., Harris DR., Coffey RM., "Comorbidity measures for use with administrative data", Medical Care, Vol.36, No.1, pp.8-27, 1998. DOI: http://dx.doi.org/10.1097/00005650-199801000-00004
  17. Quan H., Li B., Couris CM., Fushimi K., Graham P., Hider P., Januel JM., Sundararajan V., "Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries", American Journal of Epidemiology, Vol.173, No.6, pp.676-682, 2011. DOI: http://dx.doi.org/10.1093/aje/kwq433
  18. HCUP, http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp

피인용 문헌

  1. A Convergence Study in the Severity-adjusted Mortality Ratio on inpatients with multiple chronic conditions vol.13, pp.12, 2015, https://doi.org/10.14400/JDC.2015.13.12.245
  2. Development of severity-adjusted length of stay in knee replacement surgery vol.13, pp.2, 2015, https://doi.org/10.14400/JDC.2015.13.2.215