348 Insoo Rheem. Building the Data Mart on Antibiotic Usage for Infection Control ORIGINAL ARTICLE Korean J Clin Lab Sci. 2016;48(4):348-354 https://doi.org/10.15324/kjcls.2016.48.4.348 pissn 1738-3544 eissn 2288-1662 Building the Data Mart on Antibiotic Usage for Infection Control Insoo Rheem Department of Laboratory Medicine, Dankook University Hospital, Cheonan 31116, Korea 감염관리를위한항생제사용량데이터마트의구축 임인수 단국대학교병원진단검사의학과 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. Key words: Infection control, Data mart, Data warehouse, Antibiotic Corresponding author: Insoo Rheem Department of Laboratory Medicine, Dankook University Hospital, 201 Manghyang-ro, Dongnam-gu, Cheonan 31116, Korea Tel: 82-41-550-6668 Fax: 82-41-550-7055 E-mail: insoo@dankook.ac.kr This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright 2016 The Korean Society for Clinical Laboratory Science. All rights reserved. Received: October 11, 2016 Revised: October 25, 2016 Accepted: October 25, 2016 서론항생제의개발로감염질환치료의획기적인전기를맞게되었으나항생제의과용및남용으로항생제내성균주들이점차증가하게되었고, 효과적인감염관리를위하여각의료기관에서는항생제감수성통계와더불어항생제사용양태의분석이필요하게되었다 [1-3]. 이에연구자는일개대학병원의항생제사용경향을분석하고결과를보고한바있었다 [4]. 이러한경험을통해느꼈던것은많은노력이들어감에도불구하고일회성결과일수밖에없는한계이 었다. 본연구는이러한제한점을극복하고자지속적인활용과빠른응답이가능한감염관리데이터마트를고안하였고다차원데이터분석이가능한온라인분석처리 (online analytical processing, OLAP) 의적용을통해서체계적이고다양한항생제사용량분석을시현하고자하였다. 감염관리의주요구성요소들과유용한평가척도는미국질병통제센터 (CDC) 지침, 대한병원감염관리학회에서권장하는내용, 단국대학교병원의감염관리지침및관련문헌들을검토하여설계하였다 [5-8]. 감염관리에필요한주요구성요소는항생제내성, 항
Korean J Clin Lab Sci. Vol. 48, No. 4, December 2016 349 생제사용량, 병원감염, 감염관련비용으로산정하여감염관리메타데이터설계에적용하였다. 일반적인데이터베이스가사건처리중심으로데이터를수립하는데비하여, 데이터웨어하우스및데이터마트는정보의분석을손쉽게할수있도록일정한주기로유용한데이터들이통합되고요약된데이터모임을말하며정보의본질이재정의된다고할수있다. 이와같이데이터웨어하우스는의사결정에필요한데이터처리기능을효율적으로지원하기위한, 양질의데이터베이스이다 [9,10]. 데이터마트는기관전체가아니라해당부서또는특정분야에관련된모든데이터를포함한다. 예를들어사용자들은병원전체가아닌특정부서에관한데이터에쉽게접근이가능하다. 이러한사실때문에데이터마트는데이터웨어하우스에비해짧은개발기간및저렴한개발비용, 그리고용이한데이터유지보수와같은장점을가지고있다. 생제사용량의목적에대한조사자료를이용하였다. 3. 연구도구데이터베이스도구는관계형데이터베이스시스템 (relational database management system) 인 Microsoft SQL 2000 (Microsoft, Redmond, WA, USA) 을사용하였다. XML 도구는 Tagfree XML Editor, Tagfree DTD Editor (Dasan Technology, Pusan, Korea) 를사용하였다. 자료분석을위한질의도구는 MS SQL에포함되어있는다차원분석도구인 OLAP을이용하였다. 4. 감염관리메타데이터설계및주요감염관리평가척도선정항생제감수성자료, 항생제사용량, 진단, 치료방법, 처치 ( 수술 ), 환자분포정보, 감염관리조사결과가메타데이터구축을위한구성요소가되며, 이들요소로부터원하는평가요소에적합한평가척도를선정하였다. 재료및방법 1. 연구설계데이터마트의구축은감염관리의구성요소및평가척도를선정후다목적마크업언어 (extensible markup language, XML) 와문서형식정의 (document type definition, DTD) 를이용하여메타데이터및데이터베이스를설계하였다. OLAP 구현은항생제사용량데이터베이스에대한모델링을실시하여데이터마트를실험구축하는순서로하였다. 2. 연구대상데이터마트및 OLAP 구현을위한실험자료는 1997년 7월한달동안의천안지역의일개대학병원의항생제사용량자료및항 5. 감염관리데이터마트 (OLAP) 설계관계형데이터베이스는개체관계도 (entity-relationship diagram, ERD) 를사용하여감염관리메타데이터설계를하였다. 이들구성요소를기반으로항생제사용량분석에대한데이터마트를스타스키마모델링을이용하여설계하였다. 결과 1. 감염관리메타데이터설계항생제사용량의평가는병동별, 과별, 종류별로구분할수있으며평가척도로는일일상용량 (defined daily dose, DDD), 치료일수, 치료기간, 병합사용일수가있다. 항생제사용량, 항생제내성, 병원감염정보에대한관련구성요소는 Table 1과같다. 환자 Table 1. Measures and variables of data mart on antibiotic usage 항생제사용량항생제내성병원감염 약제 ( 항생제 ) 처방정보환자정보환자기본정보환자이력정보환자병력정보 : 진단, 처치, 퇴원요약항생제정보항생제기본정보 : 코드, 일반명, 용량, 용법항생제특성정보 : 종류, 적응증 ATC/DDD 미생물검사결과정보검체 ( 환자 ) 정보 : 처방 / 채취 / 접수감수성항생제이력정보검체종류정보균명정보항생제검사관련정보종류 (MIC, Disk, E-test) 차수 (1 차, 2 차 ) 감수성판정기준 환자기본정보검체정보감염관련정보 : 감염일, 부위, 경과항생제사용관련 : 종류, 사용량미생물검사결과 : 검체, 균명, 감수성감염위험요소 IV Catheter Hyperalimentation A-line Foley catheter Intubation Tracheostomy CNS shunt 특수검사및처치
350 Insoo Rheem. Building the Data Mart on Antibiotic Usage for Infection Control 정보는등록번호, 입원일, 퇴원일, 입원실변경이력, 생년월일, 성별, 진단명, 수술명, 수술일, 퇴원요약정보로구성하였다. 감염관련비용과관련된구성요소는검사비, 약제비, 처치료, 병실료, 진단명, 수술명, 감염관련추가재원일수, 감염관련추가진료비용으로열거되었다. 2. 문서형식정의 (DTD) 설계감염관리데이터마트의 XML DTD 상위요소는항생제내성정보, 항생제사용량정보, 감염정보, 검사정보로구성하였다. 1) 항생제내성정보의개체관계도항생제내성정보의개체관계도는 Fig. 1과같다. 2) 항생제사용량정보의개체관계도항생제사용량정보의개체관계도는 Fig. 2와같다. 3) 항생제사용량정보의문서형식정의항생제사용량정보의문서형식정의 (DTD) 는 Fig. 3와같다. 4) 스타스키마모델적용을위한항생제사용량정보의개체관계도항생제사용량정보를스타스키마에적용하기위한데이터베이스의설계는 Fig. 4와같다. 3. OLAP 모델링 1) 항생제사용량큐브의구조스타스키마는처리의중심이되는사실테이블과시간데이터를포함하는차원테이블들로구성된다. 항생제사용량분석을위한큐브는다음과같은스타스키마구조로설계하였다 (Fig. 5). 2) 항생제사용량사실테이블의스타스키마항생제사용량스타스키마는 Fig. 6과같이설계되었다. 3) 항생제사용량 OLAP에서큐브탐색의예 1997년 7월한달동안의항생제사용량자료에대한 OLAP를구현하였고 Microsoft SQL 2000의큐브브라우저를이용하여자료를탐색하였다. Fig. 7은한달동안일반외과수술이시행된남자환자에서비경구로투여된항생제사용량을종류별로산출한예이다. 투여목적 (Use) 에따른항생제종류별로항생제사용일수를집계하였다. 세팔로스포린계열항생제의경우예방적대치료적사용의비 (prophylactic vs. therapeutic ratio) 는 3.9 : 1 (4,245일 : 1,089 일 ) 이었다 (Fig. 8). Fig. 1. Entity-relationship diagram (ERD) of information on antibiotic resistance. Fig. 2. Entity-relationship diagram (ERD) of information on antibiotic utilization.
Korean J Clin Lab Sci. Vol. 48, No. 4, December 2016 351 Fig. 3. Document type definition (DTD) for antibiotic utilization. Fig. 5. Cube structure of antibiotic usage database. Fig. 4. Entity-relationship diagram (ERD) for antibiotic usage database. 고찰 병원의질향상평가의여러기준중항생제의사용은중요한지표로서미국에서는지속적인연구가보고되고있다 [11,12]. 국내에서도감염관리와항생제사용의평가및제한항생제사용의결과등에대한보고가있고 [13-16], 병원의질향상에관한정부및각병원단체들의관심이증가하고있으며항생제의올바른사용에관한보고가있었다 [3,17,18]. 국내현실에맞는항생제의적절한사용을위한전략적목표와지표를마련하기위하여국내항생제사용의현황을파악하는것이중요하다. 본연구는이러한시의에부응하여감염관리의질향상을위한방법론적접근을시도하고자하였다. Fig. 6. Entity-relationship diagram (ERD) for star schema of antibiotic usage. 2003년 Wisniewski 등은 1998년부터 5개년계획으로진행된 Chicago Antimicrobial Resistance Project (CARP) 구축을보고하였다 [5]. CARP는항생제내성관리를목적으로하여병원감염, 항생제내성, 항생제사용량및비용활용의분석을포함하는본격적인감염관리를위한데이터웨어하우스라고볼수있다. 2000년대전후로고객관계관리 (Customer relationship management, CRM) 시스템구축을위한방안으로데이터웨어하우스가기업에활발하게도입되었는데, 이것은컴퓨터와정보통신기술의발전과고객관계를공동창조의파트너로보는비즈니스패러다임의변화
352 Insoo Rheem. Building the Data Mart on Antibiotic Usage for Infection Control Fig. 7. Example 1 of OLAP query on antibiotic usage. Parenteral antibiotics administered to male patients on July 1997. Fig. 8. Example 2 of OLAP query on antibiotic usage. The prophylactic versus therapeutic ratio of cephalosporin antibiotics was 3.9 : 1 (4,245 days : 1,089 days). 에따른결과라고볼수있다. CARP 시스템도데이터웨어하우스의활용이주목받던이시기에구축되었으며, 데이터웨어하우스의특징인통계량산출및활용의자동화구현을통한항생제내성관리가목적이었다. 감염분야에서통계량집계와보고의자동화수립에는이전에도개별적인노력이있었지만 [19-22], 데이터웨어하우스는데이터자동전송및자동보고등의기능을포함하는종합적인데이터처리자동화시스템이다. 그러나데이터웨어하우스는 기업또는기관의업무처리를위한기본정보시스템에부가적인기능으로추가적인하드웨어와소프트웨어비용이소요되므로주로대규모프로젝트에서활용되며전사적인활용을목적으로구축되는것이보통이다. 따라서감염관리에특화된개별적인데이터웨어하우스나데이터마트구축사례는많지는않았으며 [5,23], 신종플루, 메르스등의신종감염병들이유행하면서최근에는감염관리에대한관심이증가하고있으며감염예측을위해데이터의실시
Korean J Clin Lab Sci. Vol. 48, No. 4, December 2016 353 간집계및처리를위한시스템구축의필요성이높아지고있다. 2011년 Chopra 등은 Detroit Medical Center 외래환자에대한검사실과약국자료를기반으로하는신종인플루엔자 A (H1N1) 감시방법을평가하였고, 2012년 Zhao 등은인플루엔자유행관찰을위한검사실기반의호흡기바이러스감시시스템 (Respiratory DataMart System) 경험을보고하였다 [24,25]. 감염관리데이터마트의적용예로항생제내성률, 환자별감염정보, 혈액감염관련정보, 중심정맥카테터사용여부파악, 항생제사용량, 그리고감염종류및항생제내성과항생제사용의연계분석을통한질관리향상및감염관련비용분석을통한적정진료의수립등을들수있다. 데이터마트구축을통한분석의장점은자료전송및통계량산출을구조화및자동화할수있어주기적인통계치산출및주문형보고가가능한데있다 [5,23], 본연구에서항생제사용량에대한 OLAP 구축을시현하여사용한결과, 데이터베이스로부터집계를위한자료를손쉽게갱신할수있었으며, 하나의화면에서다양한분석을할수있음을알수있었다. 본연구는항생제사용량에대한자료만을 OLAP 구축의예로살펴보았으므로항생제내성등의자료와의연관성분석은본연구에서실시하지못하여추가연구가필요하다. 항생제사용량정보를포함한감염관리데이터마트구축후의다음단계로는구축된정보를기반으로임상진료시직접적인도움이되는시스템을구축, 질병조사의자동화, 각종감염관련보고를자동화시키는사업들이될것이며, 데이터마이닝기법을이용하면더많은감염관리에대한숨은지식의발굴이가능하고이를활용함으로써진일보한진료의질향상에기여하게될것으로사료되었다. 요약병원정보시스템에저장되어있는자료들은적절성평가및질관리를향상시키는데있어많은잠재력을가지고있으며이를기반으로하는데이터웨어하우스의구축은질관리의향상과임상진료에많은도움을줄수있는것으로알려져있다. 본연구는감염관리정보의체계적이고효과적인자료분석을위한일환으로항생제사용량분석이가능한데이터마트를구축하였다. 감염관리의구성요소및평가척도를선정후 XML DTD 방법으로메타데이터를설계하였고모델링을통해데이터마트를구축하여항생제사용량분석을위한다차원분석도구인 OLAP를시현하였다. 실험자료는 1997년 7월한달동안의천안지역의일개대학병원의항생제사용량자료를이용하였다. 감염관리메타데이터의상위요소는항생제내성정보, 항생제사용량정보, 감염정보, 검사정보, 환자정보및감염관련비용으로서구성하였다. 이중항생제사용량정보를 스타스키마에적용하기위한데이터베이스의설계를하여데이터마트를구축하였다. 그리고일개월간사용된항생제사용량에대해 OLAP을시현하였다. 본연구는 XML과 OLAP 기술의구현을통해항생제사용량에대한감염관리데이터마트를수립하였다. 개념적이고구조화된데이터마트의구축은감염관리정보에대해신속하고다양한분석을제공할것으로사료되었다. Acknowledgements: None Funding: None Conflict interest: None 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. 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. 6. Garner JS, Jarvis WR, Emori TG, Horan TC, Hughes JM. CDC definitions for nosocomial infections. Am J Infect Control. 1988;16:128-140. 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. 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. 12. Weinstein RA. Nosocomial infection update. Emerg Infect Dis. 1998;4:416-420. 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.
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