Optimizing triage consistency in Australian emergency departments: The Emergency Triage Education Kit

Gerdtz, Marie; Collins, Marnie; Chu, Matthew; Grant, Audas W.; Tchernomoroff, Robin; Pollard, Cecily; Harris, Judy; Wassertheil, Jeff

doi:10.1111/j.1742-6723.2008.01089.x

Cited by 53 publications

(58 citation statements)

References 23 publications

(62 reference statements)

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“…6 A recent review of the ATS concluded that 'the ATS per se is insufficient to ensure acceptable inter-rater reliability, particularly during busy periods in the ED, given the over-emphasis of the ATS on key outcomes'. 7 The inter-rater reliability of the ATS for mental health patients in ED is particularly inadequate.…”

Section: Discussionmentioning

confidence: 99%

Emergency department waiting times: do the raw data tell the whole story?

Green¹,

Dawber²,

Masso³

et al. 2014

Aust. Health Review

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Objective. To determine whether there are real differences in emergency department (ED) performance between Australian states and territories.Methods. Cross-sectional analysis of 2009À10 attendances at an ED contributing to the Australian non-admitted patient ED care database. The main outcome measure was difference in waiting time across triage categories.Results. There were more than 5.8 million ED attendances. Raw ED waiting times varied by a range of factors including jurisdiction, triage category, geographic location and hospital peer group. All variables were significant in a model designed to test the effect of jurisdiction on ED waiting times, including triage category, hospital peer group, patient socioeconomic status and patient remoteness. When the interaction between triage category and jurisdiction entered the model, it was found to have a significant effect on ED waiting times (P < 0.001) and triage was also significant (P < 0.001). Jurisdiction was no longer statistically significant (P = 0.248 using all triage categories and 0.063 using only Australian Triage Scale 2 and 3).Conclusions. Although the Council of Australian Governments has adopted raw measures for its key ED performance indicators, raw waiting time statistics are misleading. There are no consistent differences in ED waiting times between states and territories after other factors are accounted for.What is known about the topic? The length of time patients wait to be treated after presenting at an ED is routinely used to measure ED performance. In national health agreements with the federal government, each state and territory in Australia is expected to meet waiting time performance targets for the five ED triage categories. The raw data indicate differences in performance between states and territories. What does this paper add? Measuring ED performance using raw data gives misleading results. There are no consistent differences in ED waiting times between the states and territories after other factors are taken into account. What are the implications for practitioners? Judgements regarding differences in performance across states and territories for triage waiting times need to take into account the mix of patients and the mix of hospitals.

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Section: Discussionmentioning

confidence: 99%

Emergency department waiting times: do the raw data tell the whole story?

Green¹,

Dawber²,

Masso³

et al. 2014

Aust. Health Review

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“…However, Gerdtz et al [6,7] found that although the improvement has not been significant, marked improvement has been obtained. In fact, the reliability of the ATS increased from a fair reliability coeffi cient of Dilley et al [2] in 1998 to moderate reliability coeffi cient of Gerdtz et al [7] in 2008, indicating that revision was considerably effective. Therefore, the ATS needs to be enhanced through the years and improved in order to reach almost perfect reliability (Figure 4).…”

Section: Discussionmentioning

confidence: 99%

“…Gerdtz et al [6] 2007 Adult NN Scenario Inter Κuw (0.38) Australia Gerdtz et al [7] 2008 Adult NN Scenario Inter Κuw (0.41) Australia Table 1. Studies on the reliability of the ATS * NN: nurse-nurse; ** Inter: inter-rater reliability; Intra: intra-rater reliability; *** Kuw: Un-weighted kappa (coeffi cient).…”

Section: Australiamentioning

confidence: 99%

“…The ATS is based on adult physiological predictors (airway, breathing, circulation, and disability). [8] Several studies [2][3][4][5][6][7] have investigated the validity and reliability of the ATS in adult and pediatric populations; but it's still unclear to what extent the ATS would support consistency in triage nurses' decision making in Australia comparing to other countries, considering the wide World J Emerg Med, Vol 6, No 2, 2015 variety of health care systems around the world. Besides, some studies [9,10] have addressed contextual influences on the triage decision making process, therefore it's necessary to discover the effect of these variables on the reliability of triage scale.…”

Section: Introductionmentioning

confidence: 99%

“…[1] The Australasian Triage Scale (ATS) is a five-level emergency department triage algorithm that has been continuously developed in Australia and subjected to several studies. [2][3][4][5][6][7] The ATS, a 5-point triage scale, has been endorsed by the Australasian College for Emergency Medicine and adopted in performance indicators by the Australian Council on Healthcare Standards. The National Triage Scale (NTS) was implemented in 1993.…”

Section: Introductionmentioning

confidence: 99%

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The reliability of the Australasian Triage Scale: a meta-analysis

Ebrahimi¹,

Heydari²,

Mazlom³

et al. 2015

World Journal of Emergency Medicine

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BACKGROUND: Although the Australasian Triage Scale (ATS) has been developed two decades ago, its reliability has not been defi ned; therefore, we present a meta-analyis of the reliability of the ATS in order to reveal to what extent the ATS is reliable.DATA SOURCES: Electronic databases were searched to March 2014. The included studies were those that reported samples size, reliability coefficients, and adequate description of the ATS reliability assessment. The guidelines for reporting reliability and agreement studies (GRRAS) were used. Two reviewers independently examined abstracts and extracted data. The effect size was obtained by the z-transformation of reliability coefficients. Data were pooled with random-effects models, and meta-regression was done based on the method of moment's estimator. RESULTS:Six studies were included in this study at last. Pooled coefficient for the ATS was substantial 0.428 (95%CI 0.340-0.509). The rate of mis-triage was less than fifty percent. The agreement upon the adult version is higher than the pediatric version. CONCLUSION:The ATS has shown an acceptable level of overall reliability in the emergency department, but it needs more development to reach an almost perfect agreement.

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