2017
DOI: 10.1590/s1679-45082017ao3964
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Emergency Severity Index: accuracy in risk classification

Abstract: ObjectiveTo verify agreement between estimative of predicted resources using the adapted Emergency Severity Index and the real amount of resources used by patients. To analyze the variables number of years since graduation, years of work experience and years of experience in emergency services especially with accurate anticipation of resources need.MethodsThis retrospective analytical study with a quantitative approach included 538 medical records of patients assisted by 11 triage nurses. Data collected were r… Show more

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Cited by 18 publications
(10 citation statements)
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References 13 publications
(15 reference statements)
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“…Few standardized objective measures exist to help EMS systems reliably classify patient risk for hospitalization or death using evidence-based criteria. Risk stratification is a valuable operational tool to predict the urgency and resource requirements for individual patients or classes of patients (1). Risk stratification can help inform prioritization of patients when demand exceeds capacity, such as in the case of severe disease outbreak like the COVID-19 pandemic.…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…Few standardized objective measures exist to help EMS systems reliably classify patient risk for hospitalization or death using evidence-based criteria. Risk stratification is a valuable operational tool to predict the urgency and resource requirements for individual patients or classes of patients (1). Risk stratification can help inform prioritization of patients when demand exceeds capacity, such as in the case of severe disease outbreak like the COVID-19 pandemic.…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…Após, os artigos serão apresentados conforme suas características e resultados encontrados na busca dos artigos selecionados conforme o tema do uso de protocolos de urgência e emergência em uma unidade hospitalar, frente às publicações e conhecimentos científicos ao tema proposto. (Silva et al, 2017).…”
Section: Resultsunclassified
“…20 Challenges to assigning ESI 1 or ESI 2 (resuscitation or emergent, respectively) and ESI 5 (nonurgent) are common, with specific difficulties reported in differentiating between ESI 2 (unstable) and ESI 3 (stable). 21 Given these challenges, machine learning (ML) approaches have been proposed to aid clinicians in various patient risk assessments. ML has been used to predict inhospital mortality, critical care (admission to an intensive care unit and/or in-hospital death), and hospitalization (direct hospital admission or transfer) in adults [22][23][24][25] and children.…”
Section: Improving Ed Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processingmentioning
confidence: 99%