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研究生:羅友聲
研究生(外文):Yu-Sheng Lo
論文名稱:基於電子病歷、監測模型與個案管理之整合性醫療相關感染監視系統
論文名稱(外文):An Integrated Healthcare-associated Infection Surveillance Information System Based on Electronic Medical Records, Detection Model, and Case Management
指導教授:劉建財劉建財引用關係
指導教授(外文):Chien-Tsai Liu
口試委員:賴飛羆邱泓文許明暉鄭伯壎
口試委員(外文):Fei-pei LaiHung-Wen ChiuMin-Huei HsuPo-Hsun Cheng
口試日期:2014-12-18
學位類別:博士
校院名稱:臺北醫學大學
系所名稱:醫學資訊研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2014
畢業學年度:103
語文別:英文
中文關鍵詞:電子病歷監測模型個案管理醫療感染監視系統
外文關鍵詞:Healthcare-associatedInfection Surveillance Information SystemElectronic Medical RecordsDetection ModelCase Management
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Healthcare-associated infections (HAIs) are infections that patients acquire during the course of receiving treatment for other conditions within a healthcare setting. HAI is the most common complication or comorbidity of hospitalized patients, and is becoming major worldwide causes of death and disability. Also, there are associated financial costs substantial to both patients and healthcare systems. HAIs are critical patient safety and healthcare quality issues. Prevention and reduction of such infection has become one of the top priorities for health care.

HAI surveillance is considered the most effective way for the prevention and reduction of HAIs. Utilization of electronic medical records (EMRs) systems can increase the work efficiency of infection control professionals (ICPs) and can reduce manual efforts, particularly in suspected case finding and automated data collection. Thus, how to take advantage of the EMRs to improve the HAI surveillance and control became challenging.

In this thesis, we focused on healthcare-associated urinary tract infection (HAUTI), which is the most common type of HAI. This study was conducted at Taipei Medical University Wan Fang Hospital (TMUWFH), which is a 730-bed, tertiary-care teaching hospital in Taiwan. Firstly, we proposed an approach to build a detection model for HAI surveillance based on the variables extracted from the EMRs. Then we mapped the CDC case definitions to a set of variables, and identified the variables whose values could be derived automatically from the EMRs of the hospital. With these variables, we constructed a detection model using a training set. Finally, we evaluated the sensitivity, specificity, and overall accuracy of the model using a testing set. Consequently, we identified six surveillance variables (fever, urine culture, blood culture, routine urinalysis, antibiotic use, and invasive devices) whose values could be derived from the EMRs of the hospital. The sensitivity, specificity and overall accuracy of the detection model were 100%, 94.61%, and 94.65%, respectively.

Moreover, we developed an integrated HAI surveillance information system (called iHAUTISIS) based on existing EMR systems of the hospital for improving the work efficiency of ICPs. The iHAUTISIS can automatically collect surveillance data relevant to HAUTI from the different EMR systems, and provides a visualization dashboard that helps ICPs make better surveillance plans and facilitate their surveillance work.

For measuring the system performance, we also created a generic model for comparing the ICPs’ work efficiency when using the existing electronic culture-based surveillance information system (eCBSIS) and iHAUTISIS, respectively. This working model can demonstrate a patient’s state (unsuspected, suspected, and confirmed) and the corresponding time spent on surveillance tasks performed by ICPs for the patient in that state. The study results showed that the iHAUTISIS performed better than the eCBSIS in terms of ICPs’ time cost. On average, its time cost was reduced by 73.27 seconds, when using iHAUTISIS (114.26 seconds) versus eCBSIS (187.53 seconds), for each patient.

Since most hospitals may adopt their EMR systems piece-by-piece to meet their functional requirements, the variables that are available in the EMRs may differ. Based on our approach, the built detection model was more flexible and can be regulated with these variables to achieve a high sensitivity, specificity, and accuracy for automatically detecting HAUTI cases. More importantly, this detection model can be extended to other types of HAI. Accordingly, 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.

An increasing number of ICPs have adopted EMR systems to support routinely HAI surveillance and control. With increased adoption of EMR systems, the development of the integrated HAI surveillance information systems would be more and more cost-effective. The iHAUTISIS adopted web-based technology that enables ICPs using laptops or mobile devices to online access a patient’s surveillance information. Therefore, the system can further facilitate the HAI surveillance and reduce ICPs’ surveillance workload.
臺北醫學大學博士學位考試委員審定書 ii
臺北醫學大學電子暨紙本學位論文書目同意公開申請書 iii
臺北醫學大學學位考試保密同意書暨簽到表 iv
誌謝 v
CONTENTS i
LIST OF FIGURES iii
LIST OF TABLES iv
ABSTRACT 1
Chapter I Introduction 4
1.1 Introduction 4
1.2 Motivation 5
1.3 Outline of the research 6
Chapter II Literature Review 7
2.1 Healthcare-associated infection 7
2.2 Surveillance of healthcare-associated Infection 8
2.3 Electronic surveillance information systems for HAI 3
Chapter III Detection Model for HAI Surveillance and Control 8
3.1 Introduction 8
3.2 Method 8
3.3 Settings 9
3.4 Data collection 9
3.5 Extraction HAUTI variables from EMR systems 9
3.6 Building the HAUTI detection model 14
3.6.1 Discriminant analysis 14
3.6.2 Construction of the DF for predicting patients with HAUTI 14
3.7 Evaluation of the HAUTI detection model 18
3.8 Summary 19
Chapter IV Development of an Integrated HAUTI Surveillance Information System 20
4.1 Introduction 20
4.2 Method 20
4.3 Settings 21
4.4 Data collection 22
4.5 Generic work model for HAI surveillance 22
4.6 An Electronic culture-based HAUTI surveillance information system (eCBSIS) 24
4.7 An Integrated HAUTI surveillance information system (iHAUTISIS) 25
4.8 Comparison of work efficiency 30
4.9 Evaluation results 32
4.10 Summary 35
Chapter V Discussions 36
Chapter VI Conclusions and Future Work 42
6.1 Conclusions 42
6.2 Future work 43
Reference 45
Appendix 56
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