The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.

INTRODUCTION: 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. DESIGN: Systematic review of prospective studies. METHODS: 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. RESULTS: 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. CONCLUSION: 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. PROTOCOL REGISTRATION: We have registered our review in PROSPERO (registration number: CRD 42023415232).

Trauma nursing competency in the emergency department: a concept analysis.

OBJECTIVES: Despite numerous published concept analyses of nursing competency, the specific understanding of trauma nursing competency in emergency departments remains limited, with no clear definition. This study aimed to clarify the definitions and attributes of trauma nursing competencies in emergency departments. DESIGN: Walker and Avant's method was used to clarify the concept of trauma nursing competency in emergency departments. DATA SOURCES: PubMed, EMBASE, CINAHL and RISS were searched from inception to 23 April 2023. ELIGIBILITY CRITERIA: Relevant studies that included combinations of the terms 'nurse', 'nursing', 'emergency', 'trauma', 'competency', 'capability' and 'skill' were selected. We restricted the literature search to English and Korean full-text publications, with no limit on the publication period; grey literature was excluded. DATA EXTRACTION AND SYNTHESIS: This study uses defining attributes, antecedents and consequences extracted through data analysis. To aid comprehension of the model, related and contrary cases of the concept were created, and empirical referents were defined. RESULTS: After excluding duplicates, irrelevant studies, incomplete texts and articles unrelated to the context and study population, 15 of the initial 927 studies were included. Five additional studies were added after a manual search of the references. The final concept analysis therefore included 20 studies. The attributes of trauma nursing competency for emergency nurses included 'rapid initial assessments considering injury mechanisms', 'priority determinations based on degrees of urgency and severity', 'clinical knowledge of trauma nursing', 'skills of trauma nursing', 'interprofessional teamwork' and 'emotional care'. CONCLUSIONS: The concept analysis revealed that it is possible to promote the enhancement and development of trauma nursing competency in emergency departments across various contexts, such as clinical practice, education, research and organisational settings. This could ultimately improve trauma nursing quality and treatment outcomes.