Utilization of Electronic Medical Records to Build a Detection Model for Surveillance of Healthcare-Associated Urinary Tract Infections

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• 作者：Yu-Sheng Lo (1)
  Wen-Sen Lee (2) (3)
  Chien-Tsai Liu (1)
  
• 关键词：Healthcare ; associated infection ; Urinary tract infection ; Electronic medical records ; Electronic health records
• 刊名：Journal of Medical Systems
• 出版年：2013
• 出版时间：April 2013
• 年：2013
• 卷：37
• 期：2
• 全文大小：140KB
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• 作者单位：Yu-Sheng Lo (1)
  Wen-Sen Lee (2) (3)
  Chien-Tsai Liu (1)
  
  1. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 WuXing Street, Taipei, 110, Taiwan
  2. Division of Internal Medicine, Department of Infection Control, Wan Fang Hospital, Taipei Medical University, 111 Sec 3, Hsing-Long Rd, Taipei, 116, Taiwan
  3. Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, 250 WuXing Street, Taipei, 110, Taiwan
  
• ISSN：1573-689X

文摘

In this study, we propose an approach to build a detection model for surveillance of healthcare-associated urinary tract infection (HA-UTI) based on the variables extracted from the electronic medical records (EMRs) in a 730-bed, tertiary-care teaching hospital in Taiwan. Firstly we mapped the CDC’s HA-UTI case definitions to a set of variables, and identified the variables whose values could be derived from the EMRs of the hospital automatically. Then with these variables we performed discriminant analysis (DA) on a training set of the EMRs to construct a discriminant function (DF) for the classification of a patient with or without HA-UTI. Finally, we evaluated the sensitivity, specificity, and overall accuracy of the function using a testing set of EMRs. In this study, six surveillance variables (fever, urine culture, blood culture, routine urinalysis, antibiotic use, and invasive devices) were identified whose values could be derived from the EMRs of the hospital. The sensitivity, specificity and overall accuracy of the built DF were 100?%, 94.61?%, and 94.65?%, respectively. Since most hospitals may adopt their EMRs piece-by-piece to meet their functional requirements, the variables that are available in the EMRs may differ. Our approach can build a detection model with these variables to achieve a high sensitivity, specificity and accuracy for automatically detecting suspected HA-UTI cases. Therefore, our approach on one hand can reduce the efforts in building the model; on the other hand, can facilitate adoption of EMRs for HAI surveillance and control.