PLoS ONE · 2014 · 80 citations · 19 references
MedicineEmergency MedicineGeriatric MedicineGeriatric CareGeriatric NursingGeriatricsEmergency TriageGeriatric EmergenciesGeriatric Emergency CareAge-specific TriageDisease Severity IndexPatient Risk TriageSeverity Index MeasurementTest DesignTriageTriage ErrorsUndertreatment Of Older Adults
OBJECTIVES: The aim of this study was to investigate the long-term effect of a teaching intervention designed to reduce undertriage rates in older ED patients. Further, to test the hypothesis that non-adherence to the Emergency Severity Index (ESI) triage algorithm is associated with undertriage. Additionally, to detect patient related risk factors for undertriage. METHODS: Pre-post-test design. The study sample consisted of all patients aged 65 years or older presenting to the ED of an urban tertiary and primary care center in the study periods. A teaching intervention designed to increase adherence to the triage algorithm. To assess, if the intervention resulted in an increase of factual knowledge, nurses took a test before and immediately after the teaching intervention. Undertriage rates were assessed one year after the intervention and compared to the pre-test period. RESULTS: In the pre-test group 519 patients were included, and 394 in the post-test-group. Factual knowledge among triage nurses was high already before the teaching intervention. Prevalence of undertriaged patients before (22.5%) and one year after the intervention (24.2%) was not significantly different (χ2 = 0.248, df = 1, p = 0.619). Sex, age, mode of arrival, and type of complaint were not identified as independent risk factors for undertriage. However, undertriage rates increased with advancing age. Adherence to the ESI algorithm is associated with correct triage decisions. CONCLUSIONS: Undertriage of older ED patients remained unchanged over time. Reasons for undertriage seem to be more complex than anticipated. Therefore, additional contributing factors should be addressed.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Christian P Subbe, R. G. Davies, Elizabeth M. Williams et al. · Anaesthesia · 2003 · 451 citations
A PREDICTION RULE TO IDENTIFY LOW-RISK PATIENTS WITH COMMUNITY- ACQUIRED PNEUMONIA
J. F Ine, T Homas E. A Uble, D Onald et al. · 2011 · 429 citations
Computing, Infectious Disease Control, Infectious Diseases +10