When a patient arrives in any emergency department, one of the first steps in their care process is triage, an opportunity for a care team member to identify critically ill patients and assign priority treatment levels.

To help differentiate patient triage levels, Scott Levin, PhD, Associate Professor of Emergency Medicine at the Johns Hopkins University School of Medicine, and a team in the Department of Emergency Medicine developed an electronic triage tool.

In a recently-published paper in the Annals of Emergency Medicine, the e-triage tool showed equal or improved identification of patient outcomes compared to ESI based on a multi-site retrospective study of nearly 173,000 emergency department visits. The study showed significant differences in patient priority levels using e-triage and ESI. For example, out of the more than 65 per cent of visits triaged to ESI Level 3, e-triage identified about 10 per cent, or more than 14,000, ESI Level 3 patients who may have benefitted from being up-triaged to a more critical priority level, such as Level 1 or 2.

These patients were at least five times more likely to experience a critical outcome, such as death, admission to the ICU or emergency surgery, and two times more likely to be admitted to the hospital. The e-triage tool was also able to increase the number of patients down-triaged to a lower priority level, such as Level 4 or 5, to help minimise low-acuity patients from waiting and overusing scarce resources.

The e-triage tool uses an algorithm to predict patient outcomes based on a systems engineering approach and advanced machine learning methods to identify relationships between predictive data and patient outcomes.

‘When a patient comes in, and we collect the patient’s information, the e-triage tool is comparing that patient to hundreds of other like patients to make a prediction on the patient’s outcome,’ Levin said.

The e-triage tool is also designed to be a decision support tool to help clinicians make better care decisions about their patients.

‘The theory behind this tool, and all clinical decision support tools, is that the tool paired with the clinician can make better predictions or better prognostics tasks like this than the tool alone or the clinician alone,’ Levin explained.

Better differentiating patients’ priority levels, can, in turn, help patients get the appropriate care they need.

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