Milan R Vaghasiya1, Margaret Murphy2, Daniel O'Flynn2, Amith Shetty2. 1. Westmead Hospital Emergency Department, Corner of Darcy and Hawkesbury Roads, Westmead, Sydney, New South Wales 2145, Australia. Electronic address: milan_aus@yahoo.com.au. 2. Westmead Hospital Emergency Department, Corner of Darcy and Hawkesbury Roads, Westmead, Sydney, New South Wales 2145, Australia.
Abstract
BACKGROUND: Emergency departments (ED) continue to evolve models of care and streaming as interventions to tackle the effects of access block and overcrowding. Tertiary ED may be able to design patient-flow based on predicted dispositions in the department. Segregating discharge-stream patients may help develop patient-flows within the department, which is less affected by availability of beds in a hospital. We aim to determine if triage nurses and ED doctors can predict disposition outcomes early in the patient journey and thus lead to successful streaming of patients in the ED. METHODS: During this study, triage nurses and ED doctors anonymously predicted disposition outcomes for patients presenting to triage after their brief assessments. Patient disposition at the 24-h post ED presentation was considered as the actual outcome and compared against predicted outcomes. RESULTS: Triage nurses were able to predict actual discharges of 445 patients out of 490 patients with a positive predictive value (PPV) of 90.8% (95% CI 87.8-93.2%). ED registrars were able to predict actual discharges of 85 patients out of 93 patients with PPV of 91.4% (95% CI 83.3-95.9%). ED consultants were able to predict actual discharges of 111 patients out of 118 patients with PPV 94.1% (95% CI 87.7-97.4%). PPVs for admission among ED consultants, ED registrars and Triage nurses were 59.7%, 54.4% and 48.5% respectively. CONCLUSIONS: Triage nurses, ED consultants and ED registrars are able to predict a patient's discharge disposition at triage with high levels of confidence. Triage nurses, ED consultants, and ED registrars can predict patients who are likely to be admitted with equal ability. This data may be used to develop specific admission and discharge streams based on early decision-making in EDs by triage nurses, ED registrars or ED consultants. Crown
BACKGROUND: Emergency departments (ED) continue to evolve models of care and streaming as interventions to tackle the effects of access block and overcrowding. Tertiary ED may be able to design patient-flow based on predicted dispositions in the department. Segregating discharge-stream patients may help develop patient-flows within the department, which is less affected by availability of beds in a hospital. We aim to determine if triage nurses and ED doctors can predict disposition outcomes early in the patient journey and thus lead to successful streaming of patients in the ED. METHODS: During this study, triage nurses and ED doctors anonymously predicted disposition outcomes for patients presenting to triage after their brief assessments. Patient disposition at the 24-h post ED presentation was considered as the actual outcome and compared against predicted outcomes. RESULTS: Triage nurses were able to predict actual discharges of 445 patients out of 490 patients with a positive predictive value (PPV) of 90.8% (95% CI 87.8-93.2%). ED registrars were able to predict actual discharges of 85 patients out of 93 patients with PPV of 91.4% (95% CI 83.3-95.9%). ED consultants were able to predict actual discharges of 111 patients out of 118 patients with PPV 94.1% (95% CI 87.7-97.4%). PPVs for admission among ED consultants, ED registrars and Triage nurses were 59.7%, 54.4% and 48.5% respectively. CONCLUSIONS: Triage nurses, ED consultants and ED registrars are able to predict a patient's discharge disposition at triage with high levels of confidence. Triage nurses, ED consultants, and ED registrars can predict patients who are likely to be admitted with equal ability. This data may be used to develop specific admission and discharge streams based on early decision-making in EDs by triage nurses, ED registrars or ED consultants. Crown
Authors: Lisa M Daniels; Atsushi Sorita; Deanne T Kashiwagi; Masashi Okubo; Evan Small; Eric C Polley; Adam P Sawatsky Journal: J Gen Intern Med Date: 2018-01-16 Impact factor: 5.128
Authors: Michael M Dinh; Saartje Berendsen Russell; Kendall J Bein; Kris Rogers; David Muscatello; Richard Paoloni; Jon Hayman; Dane R Chalkley; Rebecca Ivers Journal: BMC Emerg Med Date: 2016-12-03