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New tool improves efficiency of emergency care
October 12, 2017, 4:51 pm
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Hospital emergency departments are often strained to the limit when responding to disasters or crises involving a lot of people. In such situations, one of the first steps that healthcare staff does on people being brought in is to conduct a triage to determine critically ill patients and assign priority treatment to them.

In most hospitals, healthcare personnel usually use an emergency severity index (ESI) to assign a score from Level 1 for patients who are the most critically sick, to Level 5 for patients who are the least sick. A patient's ESI level determines how quickly and in which emergency department the patient will be seen. Currently, this decision is completely subjective with nurses and physicians making a quick assessment based solely on their clinical judgment. Most patients are assigned Level 3 without really differentiating between whether they need immediate medical attention or could wait for a while.

Now scientists at John Hopkins University in the US have developed a new electronic triage tool that demonstrates improved identification of patient outcomes compared to ESI. The tool was developed following 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 percent of visits triaged to ESI Level 3, e-triage identified about 10 percent, or more than 14,000 patients who may have benefitted from being up-triaged to a more critical priority level, such as Level 1 or 2. 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 minimize low-acuity patients from waiting and overusing scarce resources.

The e-triage tool uses an algorithm to predict patient outcomes based on advanced machine learning methods to identify relationships between predictive data and patient outcomes. When a patient arrives, the e-triage tool compares that person’s health status to that of hundreds of other similarly afflicted patients in a database to make a prediction on the severity of the patient's condition.

Machine-based learning takes full advantage of electronic health records and allows a precision of outcomes that was previously not possible, said the research team behind the tool. They added that 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 than the tool alone or the clinician alone can make.

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