Korean Journal of Clinical Laboratory Science (대한임상검사과학회지)

Korean Society for Clinical Laboratory Science (대한임상검사과학회)
권호 표지
DOI QR코드

DOI QR Code

Building the Data Mart on Antibiotic Usage for Infection Control

감염관리를 위한 항생제 사용량 데이터마트의 구축

  • Rheem, Insoo (Department of Laboratory Medicine, Dankook University Hospital)
  • 임인수 (단국대학교병원 진단검사의학과)
  • Received : 2016.10.11
  • Accepted : 2016.10.25
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Data stored in hospital information systems has a great potential to improve adequacy assessment and quality management. Moreover, an establishment of a data warehouse has been known to improve quality management and to offer help to clinicians. This study constructed a data mart that can be used to analyze antibiotic usage as a part of systematic and effective data analysis of infection control information. Metadata was designed by using the XML DTD method after selecting components and evaluation measures for infection control. OLAP-a multidimensional analysis tool-for antibiotic usage analysis was developed by building a data mart through modeling. Experimental data were obtained from data on antibiotic usage at a university hospital in Cheonan area for one month in July of 1997. The major components of infection control metadata were antibiotic resistance information, antibiotic usage information, infection information, laboratory test information, patient information, and infection related costs. Among them, a data mart was constructed by designing a database to apply antibiotic usage information to a star schema. In addition, OLAP was demonstrated by calculating the statistics of antibiotic usage for one month. This study reports the development of a data mart on antibiotic usage for infection control through the implementation of XML and OLAP techniques. Building a conceptual, structured data mart would allow for a rapid delivery and diverse analysis of infection control information.

병원정보시스템에 저장되어 있는 자료들은 적절성 평가 및 질관리를 향상시키는 데 있어 많은 잠재력을 가지고 있으며 이를 기반으로 하는 데이터웨어하우스의 구축은 질 관리의 향상과 임상진료에 많은 도움을 줄 수 있는 것으로 알려져 있다. 본 연구는 감염관리 정보의 체계적이고 효과적인 자료 분석을 위한 일환으로 항생제 사용량 분석이 가능한 데이터마트를 구축하였다. 감염관리의 구성요소 및 평가 척도를 선정 후 XML DTD 방법으로 메타데이터를 설계하였고 모델링을 통해 데이터마트를 구축하여 항생제 사용량 분석을 위한 다차원 분석 도구인 OLAP를 시현하였다. 실험 자료는 1997년 7월 한 달 동안의 천안 지역의 일개 대학병원의 항생제 사용량 자료를 이용하였다. 감염관리 메타데이터의 상위요소는 항생제 내성 정보, 항생제 사용량 정보, 감염 정보, 검사 정보, 환자 정보 및 감염 관련 비용으로서 구성하였다. 이 중 항생제 사용량 정보를 스타 스키마에 적용하기 위한 데이터베이스의 설계를 하여 데이터마트를 구축하였다. 그리고 일 개월 간 사용된 항생제 사용량에 대해 OLAP을 시현하였다. 본 연구는 XML과 OLAP 기술의 구현을 통해 항생제 사용량에 대한 감염관리 데이터마트를 수립하였다. 개념적이고 구조화된 데이터마트의 구축은 감염관리 정보에 대해 신속하고 다양한 분석을 제공할 것으로 사료되었다.

Keywords

References

  1. Jarvis W. Selected aspects of the socioeconomic impact of nosocomial infections: morbidity, mortality, cost, and prevention, Infect Control Hosp Epidemiol. 1996;17:552-557. https://doi.org/10.1017/S019594170000480X
  2. Daikos GL, Cleary T, Rodriguez A, Fischl MA. Multidrug-resistant tuberculous meningitis in patients with AIDS. Int J Tuberc Lung Dis. 2003;7:394-398.
  3. Kim D, Kim N, Lee S. Technique and analysis of antibiotics use in national insurance claim data: Focused on antibiotics without DDD of WHO. Kor J Clin Pharm. 2007;17:19-32.
  4. Rheem I, Choi DG, Park WS, Choi EK, Pai H. Individual drug day (IDD) as a measure of antibiotic usage in a university hospital : A new approach. J Korean Soc Chemother. 1998;16:51-60.
  5. Wisniewski MF, Kieszkowski P, Zagorski BM, Trick WE, Sommers M, Weinstein RA. Development of a clinical data warehouse for hospital infection control. J Am Med Inform Assoc. 2003;10:454-462. https://doi.org/10.1197/jamia.M1299
  6. Garner JS, Jarvis WR, Emori TG, Horan TC, Hughes JM. CDC definitions for nosocomial infections. Am J Infect Control. 1988;16:128-140. https://doi.org/10.1016/0196-6553(88)90053-3
  7. Chopra I, Hodgson J, Metcalf B, Poste G. New approaches to the control of infections caused by antibiotic-resistant bacteria. An industry perspective. JAMA. 1996;275:401-403. https://doi.org/10.1001/jama.1996.03530290071040
  8. Korean Society for Nosocomial Infection Control. Korean nosocomial infections surveillance manual 2006. 1st ed. Seoul: Gukjin; 2006.
  9. Cho JH. Data Warehousing and OLAP. Seoul: Dae Chung; 1996.
  10. Cho JH and Park SJ. The OLAP Technology. Seoul: Sigma Insight Com; 2003.
  11. Haley RW, Culver DH, White JW, Morgan WM, Emori TG. The nationwide nosocomial infection rate. A new need for vital statistics. Am J Epidemiol. 1985;121:159-167, 182-205. https://doi.org/10.1093/oxfordjournals.aje.a113988
  12. Weinstein RA. Nosocomial infection update. Emerg Infect Dis. 1998;4:416-420. https://doi.org/10.3201/eid0403.980320
  13. Kim JM. National survey on the current status of antibiotic use in Korea and a proposition on the appropriate use of antibiotics. J Korean Soc Chemother. 2001;19:105-195.
  14. Pai H. Strategies for optimal antibiotics usage to control antimicrobial-resistant microorganisms in hospital. J Korean Soc Chemother. 1997;15:9-18.
  15. Kim SM, Lee NY, Chung JO. Prevalence and antimicrobial susceptibility against imipenem-resistant Acinetobacter baumannii and MRSA isolates from intensive care unit patients. Korean J Clin Lab Sci. 2001;33:65-73.
  16. Chong MS, Lee K. Status of infection control in Jeju-area general hospitals. Korean J Clin Lab Sci. 2016;48:130-136. https://doi.org/10.15324/kjcls.2016.48.2.130
  17. Lee J, Lee S, Kim Y, Shin WG, Lee BK, Lee HJ. Drug use evaluation of vancomycin in pediatric patients (II) - The effect of approval for vancomycin use. J Korean Soc Qual Assur Health Care. 1994;1:32-43.
  18. Kim SH, Choi HS, Kim HS, Shin WG, Shon IJ, Cho NC, et al. Drug use evaluation on ceftazidime. J Korean Soc Qual Assur Health Care. 1994;1:44-54.
  19. Evans RS, Larsen RA, Burke JP, Gardner RM, Meier FA, Jacobson JA, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA. 1986;256:1007-1011. https://doi.org/10.1001/jama.1986.03380080053027
  20. Heininger A, Niemetz AH., Keim M, Fretschner R, Doering G, Unertl K. Implementation of an Interactive computer-assisted infection monitoring program at the bedside. Infect Control Hosp Epidemiol. 1999;20:444-447. https://doi.org/10.1086/501652
  21. Overhage JM, Suico J, McDonald CJ. Electronic laboratory reporting: barriers, solutions and findings. J Public Health Manag Pract. 2001;7:60-66.
  22. Panackal AA, M'Ikanatha NM, Tsui FC, McMahon J, Wagner MM, Dixon BW, et al. Automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. Emerg Infect Dis. 2002;8:685-691. https://doi.org/10.3201/eid0807.010493
  23. Trick WE. Building a data warehouse for infection control. Am J Infect Control. 2008;36:S75-81. https://doi.org/10.1016/j.ajic.2007.07.004
  24. Chopra T, Binienda J, Mohammed M, Shyamraj R, Long P, BachD, et al. A practical method for surveillance of novel H1N1 influenza using automated hospital data. Infect Control Hosp Epidemiol. 2011;32:700-702. https://doi.org/10.1086/660200
  25. Zhao H, Green H, Lackenby A, Donati M, Ellis J, Thompson C, et al. A new laboratory-based surveillance system (Respiratory DataMart System) for influenza and other respiratory viruses in England: results and experience from 2009 to 2012. Euro Surveill. 2014;19(3):pii=20680.