Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

Abstract

Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.

The accuracy of fully automated algorithms for surveillance of healthcare-associated urinary tract infections in hospitalized patients

“…An algorithm relying on microbiology and antibiotics performed slightly better but would result in overestimation of the incidence. These findings correspond with the results of a recent systematic review on the performance of different algorithms for automated surveillance systems of HCAIs, including UTI, concluding that ICD coding has a low sensitivity and that microbiology results alone are typically not sufficient [20]. More sophisticated algorithms using more parameters and techniques such as NLP had better performances, which was also the case in this study.…”

Natural outbreaks and bioterrorism: How to deal with the two sides of the same coin?

“…The implementation of information technologies leaves room for improvement in the outbreak detection process [ 54 ] as more and more stakeholders of the health care system use informatics tools in their daily practice. Yet, considerable efforts have been made on information technologies and electronic query of a data set to improve the efficiency of surveillance [ 55 ]. It's an imperative prerequisite for the implementation of an electronically assisted surveillance.…”

Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections

“…Increasing efforts to digitalize healthcare data have created novel opportunities for automating the surveillance process and thereby increasing efficiency and standardizing surveillance methods. However, to date, automated surveillance (AS) methods have been mainly developed by individual healthcare facilities and vary widely between them [9,10]. Practical guidance on how to implement large-scale AS and increase standardization is much needed.…”