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

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.