Tool to Improve Care in EDs

“With Increases in annual visits to emergency departments, declines in capacity have led to unprecedented levels of crowding and consequential delays in care,” said Scott Levin PhD, Associate Professor of Emergency Medicine at Johns Hopkins University (JHU) School of Medicine. “Therefore, emergency departments have to very quickly assess whether a patient is in need of real critical time sensitive treatment versus a patient who can wait.”

Today, nurses and physicians typically use the Emergency Severity Index (ESI)) during triage to assign a score from Level 1 for patients who are the most critically sick to Level 6 for patients that are the least sick. A patient’s ESI level determines where in the ED the patient will be seen, places the patient in a queue, and influences provider decision-making throughout the patient’s care process.

To help determine patient triage levels, Levin and the team in the Johns Hopkins Department of Emergency Medicine team have developed an electronic triage have developed an electronic triage tool.

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

A published paper on the electronic triage tool recently appearing in the “Annuals of Emergency Medicine”, showed that the e-triage tool showed equal or improved identification of patient outcomes, as compared to ESI-based on a multi-site retrospective study of nearly 173,000 ED visits. The study showed significant positive difference in patient priority levels using e-triage and ESI.

E-triage is currently being used at the Johns Hopkins Hospital and Howard County General Hospital both member hospitals of Johns Hopkins Medicine. The tool is continuing to be prospectively evaluated with preliminary results suggesting that there is improvement in detecting patients with critical outcomes.

Grant funding for this study was provided by the Agency for Healthcare Research and Quality (AHRQ) (R21HS023641) and the National Science Foundation (NSF) SBIR grant (1621699).

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