Title

院內血流感染監視輔助系統之開發與建置

Translated Titles

Development of a Guideline based System for Bloodstream Infection Surveillance and Control

DOI

10.29829/TJTAMI.201112.0003

Authors

江政家(Jheng-Jia Jiang);張秀雯(Siou-Wun Chang);蔡國卿(Kuo-Ching Tsai);劉建財(Chien-Tsai Liu)

Key Words

院內感染 ; 血流感染 ; 演算法 ; 自動化 ; HAI ; Healthcare-associated infection ; Bloodstream infection ; Nosocomial surveillance systems

PublicationName

醫療資訊雜誌

Volume or Term/Year and Month of Publication

20卷3期(2011 / 12 / 01)

Page #

41 - 52

Content Language

繁體中文

Chinese Abstract

院內血流感染的發生不僅提高醫療院所的治療成本,也危害到病人的生命安全是醫療品質不可忽視的重要環節,隨著日常的臨床作業逐漸資訊化,院內血流感染監視作業需要收集並查閱各種相關資料,依據收案標準調查確認感染個案的發生。然而審查個案所需要的相關資料是分散於各醫療系統之間,為減少資料查詢所消耗的人工作業時間,電腦化的整合監視輔助系統可協助收集各項資料提供感控人員判斷院內血流感染個案的發生。本研究將建置院內血流感染監視輔助系統,系統包含三個模組:( 1 ) 自動化資料收集模組;( 2 ) 規則判斷模組;( 3 ) 資料作業模組。自動化資料收集模組是將院內血流感染監視資料透過網路從相關系統中自動收集。院內血流感染需要收集的資料內容則依據院內感染收案標準流程與感染個案資料,包括陽性檢驗報告、微生物的抗生素敏感性試驗報告、用藥資料、症狀資料、住院資料。規則判斷模組透過探討將院內血流感染的收案標準設計成電子化判斷規則演算法的步驟,設計電子化判斷規則,規則包含院內血流感染的判斷規則與其他的可能感染部位的排除規則,然而結合自動化資料收集模組與規則判斷模組可推斷疑似的院內血流感染個案。資料作業模組則將分析後的資料,以不同的呈現介面提供院內感控人員執行監視和調查工作。院內血流感染監視輔助系統開發完成後,以2011年3月-5月的住院病人資料進行回溯性研究(retrospective study)分析。我們將以感控人員調查確認的實際個案數為標準,討論本系統的靈敏度(sensitivity)和特異度(specificity)。2011年3月- 5月住院病人共有6334位,院內血流感染實際個案數有54件。本系統推斷的疑似院內血流感染個案數為73件,其中實際個案數有54件,而29件為推斷錯誤。因此,本系統的敏感度(sensitivity)為100%,特異度(specificity)為98%。不過,研究結果顯示本監視輔助系統所提示的疑似個案共有73個,以此73個案所需要調查的相關陽性檢驗紀錄只需要確認473筆,與原本的陽性檢驗清單紀錄(3209筆)相較而言,可節省85%的查詢時間。

English Abstract

Healthcare-Associated Bloodstream Infection (HA-BSI) is common in health care settings. In order to detect HA-BSI cases infection control professionals (ICPs) have to collect relevant data for justifying whether the cases are true infection cases. Because the data are distributed on different hospital information systems, data collection is time-consuming and costly. It is important to build a bloodstream infection surveillance system based on the CDC's guideline. The system would provide assistance in data collection and case finding for nosocomial infection surveillance, control and prevention.The bloodstream infection surveillance system includes three modules: ( 1 ) Automated data collection module is to collect data on hospital information systems. The data include laboratory reports, susceptibility tests, medication records, symptom records, and administrative data. ( 2 ) Electronic surveillance algorithm module is for creating electronic surveillance algorithms based on the existing manual surveillance practices methods, and detecting suspected BSI cases. ( 3 ) Bloodstream infection data view module is to provide infection control professionals to check patients' clinical data and laboratory test results. In this study, we performed retrospective analysis to validate the system. The subjects were the patients admitted to a regional hospital from March 1 to May 30, 2011. Sensitivity and specificity are used for comparison between the suspected BSI cases generated by the system and the true BSI cases found by the infection control professionals. The total number of patients involved in the study was 6334. Among them, the number of positive results of culture tests was 3209, and the number of true BSI cases was 54. The system generated 73 suspected cases. Thus, sensitivity and specificity of the system were 100% and 98%, respectively. The ICPs had to investigate only 473 positive lab tests for those 73 patients. Compare to the manual surveillance method, which investigates patients with the positive results of culture tests, the ICPs saved up to 85% of the investigation effort with the assistance of the system.

Topic Category 醫藥衛生 > 醫院管理與醫事行政
Times Cited
  1. 李傳博(2017)。院內感染監控之商業智能系統建置。臺北醫學大學醫學資訊研究所學位論文。2017。1-72。