SCHAUMBURG, Ill., Dec. 18, 2019 /PRNewswire/ -- Signaling an important next step in the evolution of emergency department triage, the Emergency Nurses Association on Wednesday announced a partnership with Silicon Valley-based Mednition to put the power of clinical data science and machine-learning behind the Emergency Severity Index triage system used by nearly every emergency nurse in the United States.

In October, ENA acquired ESI, which is the leading triage resource relied upon by nurses to assess patient acuity based on their presentation in the ED and the expected level of care the patient will require. In partnership with ENA, Mednition will provide research expertise and cutting-edge data analysis tools that use real-time information to update and improve ESI, allowing ENA to partner with nurses to enhance their triage skills and decision-making abilities.

ENA's collaboration with Mednition is a major milestone in the association's plan to update, enhance and expand the use of ESI. As it has done with previous education tools, ENA also plans to form a work group comprised of emergency nurse experts who will be focused on ESI's future development.
"Through the acquisition of ESI and our partnership with Mednition, ENA is uniquely positioned to drive the advancement of triage in the emergency department and ultimately improve patient care," said ENA President Patti Kunz Howard, PhD, RN, CEN, CPEN, TCRN, NE-BC, FAEN, FAAN. "This will undoubtedly help emergency nurses put the patients they see on the best course of treatment to attain better outcomes."

The partnership is also a recognition of the need in today's emergency department to use clinically validated data, aided by machine learning, to fuel rapid practice improvement. With the dynamic support of Mednition's expertise in this collaboration, ENA will focus its triage vision on five key areas:

Assessment, refinement and deployment of an updated ESI model
Initial triage training and education updates
Research and learning for the advancement of the emergency nursing profession
Standardization of nursing data to advance sharing of that data
Ensuring modern triage best practices and skill development
Mednition's real-time machine learning-based clinical decision support technology is currently in use at Adventist Health White Memorial in Los Angeles, California. AHWM nurses assess an average of 160 patients per day and have used the Mednition's KATE system to assist in acuity assessment since the start of 2019.

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