Abstract
ObjectivesSeveral automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results.MethodsWe developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies.ResultsTwenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%.ConclusionEven if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results.
Highlights
Hospital information systems are goldmines for infection preventionists and epidemiologists
The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4)
The large amount of data that they contain can help to detect adverse events, highlight risk factors, and evaluate the effectiveness of preventive actions [1]. These big data differ substantially from the ones that epidemiologists traditionally handle, but thanks to innovative methods borrowed from machine learning, data mining and natural language processing, they can be used to improve the quality and safety of healthcare [2]
Summary
Hospital information systems are goldmines for infection preventionists and epidemiologists. The large amount of data that they contain can help to detect adverse events, highlight risk factors, and evaluate the effectiveness of preventive actions [1]. These big data differ substantially from the ones that epidemiologists traditionally handle, but thanks to innovative methods borrowed from machine learning, data mining and natural language processing, they can be used to improve the quality and safety of healthcare [2]. Identifying nosocomial infections is useful to detect hospital outbreaks, which, given the potential morbidity, disorganization and cost that they can cause, represent a menace to patients, caregivers and healthcare systems [6,7]. Case identification is only the first step in the surveillance process, and epidemiologists must search for patterns that substantiate epidemic spread [8]
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