Development, implementation, and impact of an automated early warning and response system for sepsis. Issue 1 (26th September 2014) - Umscheid, Craig A.; Betesh, Joel; VanZandbergen, Christine; Hanish, Asaf; Tait, Gordon; Mikkelsen, Mark E.; French, Benjamin; Fuchs, Barry D.
<x xml:space="preserve">Abstract</x> BACKGROUND Early recognition and timely intervention significantly reduce sepsis‐related mortality. OBJECTIVE Describe the development, implementation, and impact of an early warning and response system (EWRS) for sepsis. DESIGN After tool derivation and validation, a preimplementation/postimplementation study with multivariable adjustment measured impact. SETTING Urban academic healthcare system. PATIENTS Adult non‐ICU patients admitted to acute inpatient units from October 1, 2011 to October 31, 2011 for tool derivation, June 6, 2012 to July 5, 2012 for tool validation, and June 6, 2012 to September 4, 2012 and June 6, 2013 to September 4, 2013 for the preimplementation/postimplementation analysis. INTERVENTION An EWRS in our electronic health record monitored laboratory values and vital signs in real time. If a patient had ≥4 predefined abnormalities at any single time, the provider, nurse, and rapid response coordinator were notified and performed an immediate bedside patient evaluation. MEASUREMENTS Screen positive rates, test characteristics, predictive values, and likelihood ratios; system utilization; and resulting changes in processes and outcomes. RESULTS The tool's screen positive, sensitivity, specificity, and positive and negative predictive values and likelihood ratios for our composite of intensive care unit (ICU) transfer, rapid response team call, or death in the derivation cohort was 6%, 16%, 97%, 26%, 94%, 5.3, and 0.9, respectively. Validation values were similar. The EWRS resulted in a statistically significant increase in early sepsis care, ICU transfer, and sepsis documentation, and decreased sepsis mortality and increased discharge to home, although neither of these latter 2 findings reached statistical significance. CONCLUSIONS An automated prediction tool identified at‐risk patients and prompted a bedside evaluation resulting in more timely sepsis care, improved documentation, and a suggestion of reduced mortality. Journal of Hospital Medicine 2015;10:26–31. © 2014 Society of Hospital Medicine