Timothy L Wiemken1, Lori Hainaut2, Heather Bodenschatz2, Ruby Varghese2. 1. Saint Louis University Center for Health Outcomes Research, St. Louis, MO. Electronic address: timothy.wiemken@health.slu.edu. 2. Department of Infection Prevention, SSM Health Saint Louis University Hospital, St. Louis, MO.
Abstract
BACKGROUND: Hand hygiene is the most important intervention to reduce the risk of transmission of pathogens in health care. Assurance of effective hand hygiene improvement campaigns includes adequate data analytics for reporting compliance. Traditional analytical approaches for monitoring hand hygiene compliance suffer from several limitations, including autocorrelation. The objective of this study was to use a novel time series anomaly detection algorithm to analyze routine hand hygiene compliance data. METHODS: Hand hygiene compliance data were collected daily by trained observers in a large academic medical center. Statistical process control p-charts were used as a comparison method of analysis per facility protocol. Time series anomaly detection was carried out using the seasonal and trend decomposition using LOESS (STL) algorithm. RESULTS: A total of 34 months of hand hygiene compliance data were analyzed. Traditional statistical process control p-charts identified over 76% of rates as special-cause variation, whereas STL identified 18% of rates as anomalous. CONCLUSIONS: This study supports the use of time series anomaly detection for the routine surveillance of hand hygiene compliance data. This method will facilitate specific and accurate feedback, helping to improve this critical approach for improving patient safety.
BACKGROUND: Hand hygiene is the most important intervention to reduce the risk of transmission of pathogens in health care. Assurance of effective hand hygiene improvement campaigns includes adequate data analytics for reporting compliance. Traditional analytical approaches for monitoring hand hygiene compliance suffer from several limitations, including autocorrelation. The objective of this study was to use a novel time series anomaly detection algorithm to analyze routine hand hygiene compliance data. METHODS: Hand hygiene compliance data were collected daily by trained observers in a large academic medical center. Statistical process control p-charts were used as a comparison method of analysis per facility protocol. Time series anomaly detection was carried out using the seasonal and trend decomposition using LOESS (STL) algorithm. RESULTS: A total of 34 months of hand hygiene compliance data were analyzed. Traditional statistical process control p-charts identified over 76% of rates as special-cause variation, whereas STL identified 18% of rates as anomalous. CONCLUSIONS: This study supports the use of time series anomaly detection for the routine surveillance of hand hygiene compliance data. This method will facilitate specific and accurate feedback, helping to improve this critical approach for improving patient safety.