Detecting serious infections early in the Emergency Department using data analytics

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Triage in the Emergency Department (ED) is an opportunity for a time critical point of identification of evolving severe sepsis. Current identification rates at triage are reported in the realm of 50-60% in the literature. Tromp et al undertook an education programme to improve identification of sepsis at triage, and their detection rates were 65%. Techniques to identify sepsis earlier may reduce the time to administration of antibiotics, source control and other resuscitative measures to improve patient outcomes. However, often the triage nurse is working under time pressures, and has limited information available to them to assist with their decision making. Hypothesis: Data analytic techniques may reveal early prompts to identify and place patients on sepsis pathways. Combining the demographic data and triage free text information inputted by the triage nurse could create prompts for the triage nurse to consider “is it sepsis?" earlier. AIMS: To create a predictive likelihood of sepsis from key words in inserted text. In future, develop as decision aid within EDIS (Emergency Department Information System) or separate triage tool. Methods: We will combine two large datasets - an Emergency Department dataset as well as the hospital admission dataset to create a way of determining whether information at the point of Emergency Department triage, may help predict the likelihood of subsequent diagnosis of a serious infection in a patient. "Big data" analytics tools will be used, and the datasets will be split into train, validate and tes components.

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