Effectiveness of the Manchester Triage System on time to treatment in the emergency department: a systematic review protocol

Cicolo, Emilia Aparecida; Nishi, Fernanda Ayache; Peres, Heloísa Helena Ciqueto; Cruz, Diná de Almeida Lopes Monteiro da

doi:10.11124/jbisrir-2016-003119

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…It consists of 52 units based on the main symptoms [16]. The key parameters, such as threat to life, pain, or consciousness disorder are defined for each of these block diagrams [26][27][28]. This sorting system uses certain important indicators as discriminators in the current flowchart [28].…”

Section: Resultsmentioning

confidence: 99%

“…The key parameters, such as threat to life, pain, or consciousness disorder are defined for each of these block diagrams [26][27][28]. This sorting system uses certain important indicators as discriminators in the current flowchart [28]. The effectiveness of this system was demonstrated in a 2012 retrospective cohort study aimed at assessment of the MTS accuracy at determining risks and priorities for the patient care within 24 hours of admission to emergency departments, which indicated that half of 139,000 patients were triaged within the recommended time (2-20 minutes), 88.4% of patients were not hospitalized, whereas 11.4% were hospitalized, and just 0.2% of cases were fatal [6,29].…”

Section: Resultsmentioning

confidence: 99%

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2021

Russ Open Med J

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Medical sorting is aimed at assessment of disease severity and has to be carried out within a short time to determine the priorities for patient care and transportation to the most appropriate place for future treatment. The goal of this study was to provide an integrative review by analyzing the publications on the most common triage systems worldwide in order to select and implement the most reliable system at emergency departments. We searched for publications relevant to our comparative analysis in evidence-based medicine databases. A total of 1,740 literary sources were identified, of which 42 were selected for analysis. Comparative analysis of different triage systems may help implementing the most efficient system in Kazakhstan. The Emergency Severity Index is considered the most reliable and accurate tool used in international practice, and it could provide a basis for introduction of triage system at emergency departments in Kazakhstan.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Untitled

2021

Russ Open Med J

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“…In demonstrating the validity of the MTS, this study's findings are consistent with those of numerous other studies in various settings and countries. The MTS has been observed to have particularly good performance for adult and paediatric patients 5,12,13 , although Zachariasse et al 14 found frequent incorrect classifications of children.…”

Section: Discussionmentioning

confidence: 99%

The Prioritization of Outpatients by Nurses Using the Manchester Triage System: A Case Analysis in An Austrian Accident Hospital

Haid¹,

Heimerl²,

Tossmann³

et al. 2021

MLU

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Background: Increasing workload in hospitals calls for professional prioritization regarding treatment urgency. The introduction of a triage system can offer assistance but requires experience and training.Objective: This study investigates whether, in the initial assessment procedure in an emergency department for trauma surgery, nurses (a) assess urgency following the triage systems' rules and (b) apply these correctly so subsequent medical diagnoses can be adequately performed. Methods:We evaluated 5,975 patient data records regarding urgency ratings given in initial nursing assessments and respective waiting times. Data was analyzed using descriptive statistics and nonparametric test procedures to investigate significant differences. Results:The results show that in 91% of cases, the waiting times reflect the urgency ratings given by the first-assessing nurses. In addition, ratings of 5,863 cases (98%) corresponded to later medical diagnoses.Conclusions: Initial assessments using the Manchester triage system is done very accurately and supports the treatment process structure. In addition, it can increase patient satisfaction and safety.

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“…The result of applying the MTS to an incoming patient is a deterministic colour-code that will identify patients at higher risk for deterioration and assign them to be cared for by medical personnel more quickly than those with lower risk [3].…”

Section: Introductionmentioning

confidence: 99%

Using emergency department triage for machine learning-based admission and mortality prediction

Tschoellitsch

Seidl²,

Böck

et al. 2023

European Journal of Emergency Medicine

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Aims Patient admission is a decision relying on sparsely available data. This study aims to provide prediction models for discharge versus admission for ward observation or intensive care, and 30 day-mortality for patients triaged with the Manchester Triage System. Methods This is a single-centre, observational, retrospective cohort study from data within ten minutes of patient presentation at the interdisciplinary emergency department of the Kepler University Hospital, Linz, Austria. We trained machine learning models including Random Forests and Neural Networks individually to predict discharge versus ward observation or intensive care admission, and 30 day-mortality. For analysis of the features’ relevance, we used permutation feature importance. Results A total of 58323 adult patients between 1 December 2015 and 31 August 2020 were included. Neural Networks and Random Forests predicted admission to ward observation with an AUC-ROC of 0.842 ± 0.00 with the most important features being age and chief complaint. For admission to intensive care, the models had an AUC-ROC of 0.819 ± 0.002 with the most important features being the Manchester Triage category and heart rate, and for the outcome 30 day-mortality an AUC-ROC of 0.925 ± 0.001. The most important features for the prediction of 30 day-mortality were age and general ward admission. Conclusion Machine learning can provide prediction on discharge versus admission to general wards and intensive care and inform about risk on 30 day-mortality for patients in the emergency department. Institutional review board Ethics Committee of the Medical Faculty at the Johannes Kepler University, Linz, Austria, Study Number 1233/2020.

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Effectiveness of the Manchester Triage System on time to treatment in the emergency department: a systematic review protocol

Abstract: The review aims to find the best available evidence on the effectiveness of the Manchester Triage System on time to treatment in the emergency department.

Cited by 10 publications

References 24 publications

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The Prioritization of Outpatients by Nurses Using the Manchester Triage System: A Case Analysis in An Austrian Accident Hospital

Using emergency department triage for machine learning-based admission and mortality prediction

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