Journal of emergency nursing : JEN : official publication of the Emergency Department Nurses Association
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Multicenter Study
Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing.
Triage is critical to mitigating the effect of increased volume by determining patient acuity, need for resources, and establishing acuity-based patient prioritization. The purpose of this retrospective study was to determine whether historical EHR data can be used with clinical natural language processing and machine learning algorithms (KATE) to produce accurate ESI predictive models. ⋯ KATE provides a triage acuity assignment 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 biases that can negatively affect triage accuracy. Future research should focus on the impact of KATE providing feedback to triage nurses in real time, on mortality and morbidity, ED throughput, resource optimization, and nursing outcomes.
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The purpose of this facility-level case report was to describe our facility's leadership process of applying the Donabedian model to structure an early response to the coronavirus disease pandemic relative to emergency care. Using the Donabedian model as a guide, both structure and process changes were implemented to maintain high-quality clinical outcomes as well as ED staff safety and engagement. ⋯ Clinical, service quality, and staff safety outcomes were evaluated to demonstrate that the collaborative changes that follow a known process improvement model can be used to address the coronavirus disease pandemic. Further study is needed to compare the outcomes of this facility-level case study with those of others to evaluate the success of the measures outlined.