Literature DB >> 17626690

The effect of training on nurse agreement using an electronic triage system.

Sandy L Dong1, Michael J Bullard, David P Meurer, Sandra Blitz, Brian R Holroyd, Brian H Rowe.   

Abstract

OBJECTIVES: Emergency department (ED) triage prioritizes patients based on urgency of care, and the Canadian Triage and Acuity Scale (CTAS) is the national standard. We describe the inter-rater agreement and manual overrides of nurses using a CTAS-compliant web-based triage tool (eTRIAGE) for 2 different intensities of staff training.
METHODS: This prospective study was conducted in an urban tertiary care ED. In phase 1, eTRIAGE was deployed after a 3-hour training course for 24 triage nurses who were asked to share this knowledge during regular triage shifts with colleagues who had not received training (n = 77). In phase 2, a targeted group of 8 triage nurses underwent further training with eTRIAGE. In each phase, patients were assessed first by the duty triage nurse and then by a blinded independent study nurse, both using eTRIAGE. Inter-rater agreement was calculated using kappa (weighted kappa) statistics.
RESULTS: In phase 1, 569 patients were enrolled with 513 (90.2%) complete records; 577 patients were enrolled in phase 2 with 555 (96.2%) complete records. Inter-rater agreement during phase 1 was moderate (weighted kappa = 0.55; 95% confidence interval [CI] 0.49-0.62); agreement improved in phase 2 (weighted kappa = 0.65; 95% CI 0.60-0.70). Manual overrides of eTRIAGE scores were infrequent (approximately 10%) during both periods.
CONCLUSIONS: Agreement between study nurses and duty triage nurses, both using eTRIAGE, was moderate to good, with a trend toward improvement with additional training. Triage overrides were infrequent. Continued attempts to refine the triage process and training appear warranted.

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Mesh:

Year:  2007        PMID: 17626690     DOI: 10.1017/s1481803500015141

Source DB:  PubMed          Journal:  CJEM        ISSN: 1481-8035            Impact factor:   2.410


  7 in total

Review 1.  Modern triage in the emergency department.

Authors:  Michael Christ; Florian Grossmann; Daniela Winter; Roland Bingisser; Elke Platz
Journal:  Dtsch Arztebl Int       Date:  2010-12-17       Impact factor: 5.594

2.  Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing.

Authors:  Marta Fernandes; Rúben Mendes; Susana M Vieira; Francisca Leite; Carlos Palos; Alistair Johnson; Stan Finkelstein; Steven Horng; Leo Anthony Celi
Journal:  PLoS One       Date:  2020-03-03       Impact factor: 3.240

3.  Developing machine learning models to personalize care levels among emergency room patients for hospital admission.

Authors:  Minh Nguyen; Conor K Corbin; Tiffany Eulalio; Nicolai P Ostberg; Gautam Machiraju; Ben J Marafino; Michael Baiocchi; Christian Rose; Jonathan H Chen
Journal:  J Am Med Inform Assoc       Date:  2021-10-12       Impact factor: 4.497

4.  Safety assessment of a redirection program using an electronic application for low-acuity patients visiting an emergency department.

Authors:  Anne-Laure Feral-Pierssens; Judy Morris; Martin Marquis; Raoul Daoust; Alexis Cournoyer; Justine Lessard; Simon Berthelot; Alexandre Messier
Journal:  BMC Emerg Med       Date:  2022-04-29

5.  The Reliability of the Canadian Triage and Acuity Scale: Meta-analysis.

Authors:  Amir Mirhaghi; Abbas Heydari; Reza Mazlom; Mohsen Ebrahimi
Journal:  N Am J Med Sci       Date:  2015-07

6.  Canadian Triage and Acuity Scale: testing the mental health categories.

Authors:  Anne-Marie Brown; Diana E Clarke; Julia Spence
Journal:  Open Access Emerg Med       Date:  2015-11-13

7.  Utilization of an Electronic Triage System by Emergency Department Nurses.

Authors:  Arwa Alumran; Ohoud Alkhaldi; Zainab Aldroorah; Zainab Alsayegh; Fatimah Alsafwani; Nisreen Almaghraby
Journal:  J Multidiscip Healthc       Date:  2020-03-31
  7 in total

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