Introduction: 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.Methods: The KATE triage model was developed using 166,175 patient encounters from two participating hospitals. The model was tested against a random sample of encounters that were correctly assigned an acuity by study clinicians using the Emergency Severity Index (ESI) standard as a guide.

Registered nurse (RN) "second victims" are RNs who are harmed from their involvement in medical errors. This study used the conceptual model nurse experience of medical errors and found a relationship between RN involvement in preventable adverse events and 2 domains of burnout: emotional exhaustion (P = .009) and depersonalization (P = .030). Support to RNs involved in preventable adverse events was inversely related to RN emotional exhaustion (P < .001) and depersonalization (P = .003) and positively related to personal accomplishment (P = .002).

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