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Enhancing triage accuracy: The influence of nursing education on risk prediction. | LitMetric

AI Article Synopsis

  • The study aimed to evaluate how well specially trained triage nurses predict risks compared to the Manchester Triage System (MTS) in an emergency department setting.
  • Conducted between June 1 and December 31, 2023, in Merano Hospital, the research involved continuous training for nurses and a comparison of their risk assessments against the MTS using statistical analysis.
  • Results showed that expert nurses significantly outperformed the MTS in predicting outcomes like hospital admission and mortality, highlighting the importance of ongoing training for effective patient care.

Article Abstract

Aim: This study aimed to compare the performance in risk prediction of various outcomes between specially trained triage nurses and the Manchester Triage System (MTS).

Design: Prospective observational study.

Methods: The study was conducted from June 1st to December 31st, 2023, at the Emergency Department of Merano Hospital. Triage nurses underwent continuous training through dedicated courses and daily audits. We compared the risk stratification performed by expert nurses with that of MTS on various outcomes such as mortality, hospitalisation, and urgency defined by the physicians. Comparisons were made using the Areas Under the Receiver Operating Characteristic curve (AUROC).

Results: The agreement in code classification between the MTS and the expert nurse was very low. The AUROC curve analysis showed that the expert nurse outperformed the MTS in all outcomes. The triage nurse's experience led to statistically significant better stratification in admission rates, ICU admissions, and all outcomes based on the physician's assessment.

Conclusions: The continuous training of nurses enables them to achieve better risk prediction compared to standardized triage systems like MTS, emphasizing the utility and necessity of implementing continuous training pathways for these highly specialised personnel.

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Source
http://dx.doi.org/10.1016/j.ienj.2024.101486DOI Listing

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