  Effective triage is critical to mitigating the effect of increased volume by
accurately determining patient acuity, need for resources, and establishing
effective acuity-based patient prioritization. The purpose of this
retrospective study was to determine whether historical EHR data can be
extracted and synthesized with clinical natural language processing (C-NLP) and
the latest ML algorithms (KATE) to produce highly accurate ESI predictive
models. An ML model (KATE) for the triage process was developed using 166,175
patient encounters from two participating hospitals. The model was then tested
against a gold set that was derived from a random sample of triage encounters
at the study sites and correct acuity assignments were recorded by study
clinicians using the Emergency Severity Index (ESI) standard as a guide. At the
two study sites, KATE predicted accurate ESI acuity assignments 75.9% of the
time, compared to nurses (59.8%) and average individual study clinicians
(75.3%). KATE accuracy was 26.9% higher than the average nurse accuracy
(p-value < 0.0001). On the boundary between ESI 2 and ESI 3 acuity assignments,
which relates to the risk of decompensation, KATE was 93.2% higher with 80%
accuracy, compared to triage nurses with 41.4% accuracy (p-value < 0.0001).
KATE provides a triage acuity assignment substantially more accurate than the
triage nurses in this study sample. KATE operates independently of contextual
factors, unaffected by the external pressures that can cause under triage and
may mitigate the racial and social biases that can negatively affect the
accuracy of triage assignment. Future research should focus on the impact of
KATE providing feedback to triage nurses in real time, KATEs impact on
mortality and morbidity, ED throughput, resource optimization, and nursing
outcomes.
