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Mistriage is important because of its potential for serious consequences, notwithstanding the beneficial effects of the emergency patient classification system employed to alleviate overcrowding in emergency departments (EDs). This study aimed to assess mistriage using the Korean Triage and Acuity Scale (KTAS) and identify factors influencing it.
Retrospective cross-sectional study.
We examined the factors influencing mistriage in the KTAS and rates of under- and over-triage. Participants were obtained by combining electronic health records with registry data from the National Emergency Department Information System. We assessed the eligibility of patients aged ≥ 15 years who visited the ED between July 1, 2022, and June 30, 2023. Using the KTAS classification criterion, two experienced experts determined the final acuity level. We employed multivariate logistic regression analysis to evaluate the factors that predict under- and over-triage.
Of 53,947 ED encounters, 1110 participants were enrolled in this study. Mistriage occurred in 207 (18.6%) patients: 88 (7.9%) had under-triage, and 119 (10.7%) had over-triage. In adjusted analyses, under-triage was associated with lower mean arterial pressure (odds ratio [OR], 5.42; 95% confidence interval [CI], 1.45–20.32) and presenting complaints of immunity or fever (OR, 3.41; 95% CI, 1.38–8.45), while over-triage was associated with advanced age (OR, 0.52; 95% CI, 0.28–0.98), pain (OR, 1.96; 95% CI, 1.18–3.25), lower KTAS experience (OR, 1.95; 95% CI, 1.08–3.51), and several specific present complaints.
By improving mistriage, the quality of emergency medical services may be enhanced through reduced costs, increased operational efficiency, and improved patient safety and satisfaction. Implementation of standardized criteria, validated triage tools, and enhanced provider training is crucial for achieving more accurate emergency triage. Additionally, establishing regulatory and financial incentives and developing realistic standards for mistriage management will optimize triage processes and ensure prompt, prioritized care.
Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field.
Systematic review of prospective studies.
CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol.
Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes.
Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health.
We have registered our review in PROSPERO (registration number: CRD 42023415232).
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