Acinetobacter calcoaceticus–Acinetobacter baumannii Complex Is Not Equal to A. baumannii

many important nonstandard indicators of patient care—can easily be extended to hand hygiene surveillance. Because logins linked to computers in patient rooms correlate with actual visits by HCWs vis-a-vis hand hygiene opportunities arising from HCW/patient contact, they can also be used to estimate temporal patterns appropriate for effectively monitoring hand hygiene activity levels. To validate our new method, we used 660 days of UIHC MICU log-in data (September 1, 2006, through June 21, 2008), restricted to those log-ins linked to patient rooms (a total of 1,757 unique users). We then counted the number of unique users who log in for every hour for each day in our data set. For each day and night shift, we then rank ordered each hour on the basis of the number of unique individual log-ins observed (the choice of this metric was motivated by our simulations of hand hygiene compliance, which show that methodologies that favor observing more unique individuals rather than more events results in a better overall estimate of unit compliance, in essence by reducing sample bias in population selection). The resulting distribution of each hour's respective rank is then calculated across the entire data set and validated against a similar rank-order statistic derived from the sensor-mote data, where each hour in the shift is ranked by median number of captured hand hygiene events.