Dataset Information

Implementation of advanced triage in the Emergency Department of high complexity public hospital: Research protocol.

ABSTRACT:

Aim

To evaluate the efficacy of advanced nurse triage based on the quality of care outcomes of patients attending the Emergency Department of a high-complexity hospital. To analyse the concept of advanced triage and the essential elements of the construct.

Design

Mixed longitudinal study, divided into 4 steps; which will include an initial qualitative step, two observational studies and finally, a quasi-experimental study.

Clinical trial registration number

NCT05230108.

Methods

Step 1 will consist of a concept analysis. Step 2 will include a mapping of advanced practice protocol terminologies. Step 3 will analyse the opinion of health professionals on advanced triage. In step 4: in the retrospective phase (n = 1095), sociodemographic and clinical variables and quality indicators such as waiting time will be analysed. After that, in the prospective phase (n = 547), advanced triage will be implemented and the two cohorts will be compared. The whole study will be carried out from January 2022 to January 2024.

Discussion

Patients classified as low complexity at triage are more vulnerable to emergency department overcrowding. The implementation of advanced triage would make it possible to respond to patient needs by offering equitable and quality healthcare, facilitating accessibility, safety and humanization of the emergency department.

SUBMITTER: Font-Cabrera C

PROVIDER: S-EPMC10170939 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

Implementation of advanced triage in the Emergency Department of high complexity public hospital: Research protocol.

Font-Cabrera Cristina C Juvé-Udina Maria Eulàlia ME Galimany-Masclans Jordi J Fabrellas Núria N Roselló-Novella Alba A Sancho-Agredano Raül R Adamuz Jordi J Guix-Comellas Eva Maria EM

Nursing open 20230131 6

<h4>Aim</h4>To evaluate the efficacy of advanced nurse triage based on the quality of care outcomes of patients attending the Emergency Department of a high-complexity hospital. To analyse the concept of advanced triage and the essential elements of the construct.<h4>Design</h4>Mixed longitudinal study, divided into 4 steps; which will include an initial qualitative step, two observational studies and finally, a quasi-experimental study.<h4>Clinical trial registration number</h4>NCT05230108.<h4> ...[more]

PMID: 36719704

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Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Project description:BackgroundEmergency department (ED) crowding is associated with negative health outcomes, patient dissatisfaction, and longer length of stay (LOS). The addition of advanced diagnostic imaging (ADI), namely CT, ultrasound (U/S), and MRI to ED encounter work up is a predictor of longer length of stay. Earlier and improved prediction of patients' need for advanced imaging may improve overall ED efficiency. The aim of the study was to detect the association between ADI utilization and the structured and unstructured information immediately available during ED triage, and to develop and validate models to predict utilization of ADI during an ED encounter.MethodsWe used the United States National Hospital Ambulatory Medical Care Survey data from 2009 to 2014 to examine which sociodemographic and clinical factors immediately available at ED triage were associated with the utilization of CT, U/S, MRI, and multiple ADI during a patient's ED stay. We used natural language processing (NLP) topic modeling to incorporate free-text reason for visit data available at time of ED triage in addition to other structured patient data to predict the use of ADI using multivariable logistic regression models.ResultsAmong the 139,150 adult ED visits from a national probability sample of hospitals across the U.S, 21.9% resulted in ADI use, including 16.8% who had a CT, 3.6% who had an ultrasound, 0.4% who had an MRI, and 1.2% of the population who had multiple types of ADI. The c-statistic of the predictive models was greater than or equal to 0.78 for all imaging outcomes, and the addition of text-based reason for visit information improved the accuracy of all predictive models.ConclusionsPatient information immediately available during ED triage can accurately predict the eventual use of advanced diagnostic imaging during an ED visit. Such models have the potential to be incorporated into the ED triage workflow in order to more rapidly identify patients who may require advanced imaging during their ED stay and assist with medical decision-making.

Project description:BackgroundTriage implementation in resource-limited emergency departments (EDs) has traditionally relied on intensive in-person training. This study sought to evaluate the impact of a novel digital-based learning strategy focused on the Interagency Integrated Triage Tool, a three-tier triage instrument recommended by the World Health Organization.MethodsA mixed methods study utilising pre-post intervention methods was conducted in two EDs in Papua New Guinea. The primary outcome was the mean change in knowledge before and after completion of a voluntary, multimodal training program, primarily delivered through a digital learning platform accessible via smartphone. Secondary outcomes included the change in confidence to perform selected clinical tasks, and acceptability of the learning methods.FindingsAmong 136 eligible ED staff, 91 (66.9%) completed the digital learning program. The mean knowledge score on the post-training exam was 87.5% (SD 10.4), a mean increase of 12.9% (95% CI 10.7-15.1%, p < 0.0001) from the pre-training exam. There were statistically significant improvements in confidence for 13 of 15 clinical tasks, including undertaking a triage assessment and identifying an unwell patient.In an evaluation survey, 100% of 30 respondents agreed or strongly agreed the online learning platform was easy to access, use and navigate, and that the digital teaching methods were appropriate for their learning needs. In qualitative feedback, respondents reported that limited internet access and a lack of dedicated training time were barriers to participation.InterpretationThe use of digital learning to support triage implementation in resource-limited EDs is feasible and effective when accompanied by in-person mentoring. Adequate internet access is an essential pre-requisite.FundingDevelopment of the Kumul Helt Skul learning platform was undertaken as part of the Clinical Support Program (Phase II), facilitated by Johnstaff International Development on behalf of the Australian Government Department of Foreign Affairs and Trade through the PNG-Australia Partnership. RM is supported by a National Health and Medical Research Council Postgraduate Scholarship and a Monash Graduate Excellence Scholarship, while PC is supported by a Medical Research Future Fund Practitioner Fellowship. Funders had no role in study design, results analysis or manuscript preparation.

Dataset Information

Implementation of advanced triage in the Emergency Department of high complexity public hospital: Research protocol.

Aim

Design

Clinical trial registration number

Methods

Discussion

Publications

Implementation of advanced triage in the Emergency Department of high complexity public hospital: Research protocol.

Similar Datasets

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