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We compared traditional Statistical Process Control (SPC) charts with novel Anomaly/Breakout Detection (ABD) charts using Twitter's Anomaly and Breakout detection algorithms for detecting out of control or anomalous HAI data.ABD charts appeared to work better than SPC charts in the context of seasonality and autocorrelation, two well-known statistical issues with HAI data.These new charts may be useful for trending of HAI data and for other quality improvement data monitoring.An open-access web application is provided for users to apply their own datasets to generate ABD and SPC charts.Although not all health care-associated infections (HAIs) are preventable, reducing HAIs through targeted intervention is key to a successful infection prevention program. To identify areas in need of targeted intervention, robust statistical methods must be used when analyzing surveillance data. The objective of this study was to compare and contrast statistical process control (SPC) charts with Twitter's anomaly and breakout detection algorithms.SPC and anomaly/breakout detection (ABD) charts were created for vancomycin-resistant Enterococcus, Acinetobacter baumannii, catheter-associated urinary tract infection, and central line-associated bloodstream infection data.Both SPC and ABD charts detected similar data points as anomalous/out of control on most charts. The vancomycin-resistant Enterococcus ABD chart detected an extra anomalous point that appeared to be higher than the same time period in prior years. Using a small subset of the central line-associated bloodstream infection data, the ABD chart was able to detect anomalies where the SPC chart was not.SPC charts and ABD charts both performed well, although ABD charts appeared to work better in the context of seasonal variation and autocorrelation.Because they account for common statistical issues in HAI data, ABD charts may be useful for practitioners for analysis of HAI surveillance data.