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

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

doi:10.11124/jbisrir-2017-003825

Cited by 17 publications

(11 citation statements)

References 33 publications

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“…Even though the MTS has been validated in a variety of scenarios, by design it does not allow to predict patient outcome, necessity of treatment at an ICU or hospital stay [4,5].…”

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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“…Even though the MTS has been validated in a variety of scenarios, by design it does not allow to predict patient outcome, necessity of treatment at an ICU or hospital stay [4,5].…”

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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“…Clinicians use the Manchester Triage System, a medical risk managing instrument, to properly accomplish patient stream once clinical need exceeds capacity (Singh & Awasthi, 2022). Assembly of emergency health care professionals from the general, paediatric, and ophthalmology emergency departments in Manchester established the Manchester Triage Group in November 1994 (Cicolo, Nishi, & Peres, 2020). A growing number of patients, ranging from those with mild illnesses and injuries to those with serious trauma and life-threatening disorders, visit emergency rooms every day.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Manchester Triage Patient Flow Model Regarding Waiting Time in Bahria International Hospital Lahore

Shaheen

Afzal

Tahir

et al. 2023

Preprint

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Some hospital emergency departments (EDs) suffer from prolonged patient wait times, which decreases hospital productivity. The factors influencing normal ED patient wait times are therefore crucial for hospital administrators to be aware of. An emergency severity index (ESI) and patient flow model for emergency departments were both tested in this causal comparison study, which was founded in the academic framework for business process optimization. Through the use of computer-generated reports, archival data records for one week from the ED of the Bahria International Hospital in Lahore, Pakistan were censused for an ED REU model for patient flow. The results of quantitative descriptive investigation showed that the patient flow model had a considerable temporal effect. The ability to decrease patient wait times while enhancing patient happiness and health outcomes is one of the social change implications. A quantitative study would produce data that would assist medical staff by shortening the wait times for patients.

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“…[6][7][8] Examples are the Manchester Triage Scale, the Australasian Triage Scale, the Canadian Triage and Acuity Scale and the South African Triage Scale (SATS). [9][10][11][12][13] Effective triage should rapidly identify patients with life-threatening conditions in need of immediate care and ensure that each patient receives similar initial assessment, facilitating further diagnostic work-up and treatment.…”

Section: Introductionmentioning

confidence: 99%

Implementing a new emergency medical triage tool in one health region in Norway: some lessons learned

et al. 2022

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BackgroundAcutely sick or injured patients depend on ambulance and emergency department personnel performing an accurate initial assessment and prioritisation (triage) to effectively identify patients in need of immediate treatment. Triage also ensures that each patient receives fair initial assessment. To improve the patient safety, quality of care, and communication about a patient’s medical condition, we implemented a new triage tool (the South African Triage Scale Norway (SATS-N) in all the ambulance services and emergency departments in one health region in Norway. This article describes the lessons we learnt during this implementation process.MethodsThe main framework in this quality improvement (QI) work was the plan–do–study–act cycle. Additional process sources were ‘The Institute for Healthcare Improvement Model for improvement’ and the Norwegian Patient Safety Programme.ResultsBased on the QI process as a whole, we defined subjects influencing this work to be successful, such as identifying areas for improvement, establishing multidisciplinary teams, coaching, implementing measurements and securing sustainability. After these subjects were connected to the relevant challenges and desired effects, we described the lessons we learnt during this comprehensive QI process.ConclusionWe learnt the importance of following a structured framework for QI process during the implementation of the SATS-N triage tool. Furthermore, securing anchoring at all levels, from the managements to the medical professionals in direct patient-orientated work, was relevant important. Moreover, establishing multidisciplinary teams with ambulance personnel, emergency department nurses and doctors with various medical specialties provided ownership to the participants. Meanwhile, coaching provided necessary security for the staff directly involved in caring for patients. Keeping the spirit and perseverance high were important factors in completing the implementation. Establishment of the regional network group was found to be important for sustainability and further improvements.

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

Cited by 17 publications

References 33 publications

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

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

Effectiveness of Manchester Triage Patient Flow Model Regarding Waiting Time in Bahria International Hospital Lahore

Implementing a new emergency medical triage tool in one health region in Norway: some lessons learned

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