The study aims to reduce the incidence of central line associated bloodstream infections (CLABSI). We design a computer model that comprises the entire process of central line insertion and maintenance. The model attempts to capture all major events in patient care from entrance to the hospital through the time at which the central line is ultimately removed. CLABSI data is analyzed to identify areas of potential increased risk of patient death from CLABSI. Specifically, we attempt to predict death among patients affected with CLABSI while minimizing Type II (false negative) error. By crafting the model to prioritize against this type of error, and thus identifying patients most likely to die, providers will have the best chance of intervening to reduce CLABSI-related deaths. The study led to implementation of reminders and protocols that result in a reduction of 18% of CLABSI over a period of 12 months. The predictive analysis identifies high-risk individuals to allow for proper intervention to prevent CLABSI-related deaths.
['Eva K. Lee', 'Michael Callahan', 'Xin Wei', 'Prashant Tailor', 'Alexander Quarshie', 'Michael D. Wright']
A systems approach to reducing central line associated blood stream infections