Over-triage occurs when considering the patient's pain in Korean Triage and Acuity Scale (KTAS)

Lee, Ji Hwan; Park, Yoo Seok; Park, In-Cheol; Lee, Hak Soo; Kim, Ji Hoon; Park, Joon Min; Chung, Sung Phil; Kim, Min Joung

doi:10.1371/journal.pone.0216519

Cited by 29 publications

(27 citation statements)

References 31 publications

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“…ureter stone with severe pain). Therefore, predictability of KTAS in a previous study may be the result of overtriage when compared with outcomes included emergency operation, the admission rate, the 7-day mortality [20]. In our study, the frequency of mistriage related to the pain scale was more related to under-triaged than to over-triage.…”

Section: Discussionmentioning

confidence: 58%

“…The major cause of mistriage in this study was the wrong application of the pain scale. In one study, a degree of pain on KTAS resulted in overtriage and had a negative influence on the predictability [20]. Patents who had uncomplicated diseases with severe pain were considered urgent but often did not have negative outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…However, the pilot sample size used in these studies was not sufficient and the cause of mistriage was not evaluated. Recently, it was reported that the overestimation of severity according to the pain criteria in KTAS [20]. Pain is an important criterion to assign KTAS level, and it may be careful not to overestimate.…”

Section: Introductionmentioning

confidence: 99%

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Triage accuracy and causes of mistriage using the Korean Triage and Acuity Scale

et al. 2019

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Purpose To identify emergency department triage accuracy using the Korean Triage and Acuity Scale (KTAS) and evaluate the causes of mistriage. Methods This cross-sectional retrospective study was based on 1267 systematically selected records of adult patients admitted to two emergency departments between October 2016 and September 2017. Twenty-four variables were assessed, including chief complaints, vital signs according to the initial nursing records, and clinical outcomes. Three triage experts, a certified emergency nurse, a KTAS provider and instructor, and a nurse recommended based on excellent emergency department experience and competence determined the true KTAS. Triage accuracy was evaluated by inter-rater agreement between the expert and emergency nurse KTAS scores. The comments of the experts were analyzed to evaluate the cause of triage error. An independent sample t-test was conducted to compare the number of patient visits per hour in terms of the accuracy and inaccuracy of triage. Results Inter-rater reliability between the emergency nurse and the true KTAS score was weighted kappa = .83 and Pearson’s r = .88 ( p < .001). Of 1267 records, 186 (14.7%) showed some disagreement (under triage = 131, over triage = 55). Causes of mistriage included: error applying the numerical rating scale (n = 64) and misjudgment of the physical symptoms associated with the chief complaint (n = 47). There was no statistically significant difference in the number of patient visits per hour for accurate and inaccurate triage (t = -0.77, p = .442). Conclusion There was highly agreement between the KTAS scores determined by emergency nurses and those determined by experts. The main cause of mistriage was misapplication of the pain scale to the KTAS algorithm.

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Section: Discussionmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 99%

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Triage accuracy and causes of mistriage using the Korean Triage and Acuity Scale

et al. 2019

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“…According to research by Lee et al [25], overestimation of the level of triage may occur since KTAS was based on the subjective evaluation of pain. In this study, using an ambulance is determined according to a patient's subjective judgment, and pain was already considered as a source of bias in the selection of the transportation mode for ED visits.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Adequacy of Prehospital Emergency Medical Services Use of Patients Who Visited Emergency Departments in Korea from 2016 to 2018: Data from the National Emergency Department Information System

Park

Kim

Yoon

et al. 2021

Emergency Medicine International

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Introduction. Proper ambulance use is important not only due to the patient’s transport quality but also because of the need for efficient use of limited resources allotted by the system. Therefore, this study was conducted to check for overuse or underuse of the ambulance system by patients who visited the emergency department (ED). Methods. In this study, a secondary data analysis was conducted using the existing database of the National Emergency Department Information System with all patients who visited EDs over the three-year study period from 2016 to 2018. The study subjects were classified into the following groups: (1) appropriate Emergency Medical Services (EMS) usage; (2) appropriate no EMS usage; (3) underuse; and (4) overuse groups. Results. Of 18,298,535 patients, 11,668,581 (63.77%) were classified under the appropriate usage group, while 6,629,954 (36.23%) were classified under the inappropriate usage group. In the appropriate EMS usage group, there were 2,408,845 (13.16%) patients. In the appropriate no EMS usage group, there were 9,259,706 (50.60%) patients. As for the inappropriate usage group, there were 5,147,352 (28.13%) patients categorized under the underuse group. On the other hand, there were 1,482,602 (8.10%) patients under the overuse group. Conclusion. There are many patients who use ambulances appropriately, but there are still many overuse and underuse. Guidelines on ambulance use are necessary for the efficient use of emergency medical resources and for the safety of patients.

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“…Five-level triage systems, including the Canadian Triage and Acuity Scale (CTAS), Manchester Triage System (MTS), and emergency severity index (ESI), are widely used [2,8,9]. The Korean Triage and Acuity Scale (KTAS) was developed in 2012 based on CTAS and has been used nationally as the ED triage tool in Korea since 2016 [11][12][13]. Although five-level triage systems are well established in ED, they need to be improved because they heavily rely on healthcare providers' subjective judgment, resulting in high variability [5,[7][8][9][10]12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis

Yun¹,

Choi²,

Park³

2021

JMIR Med Inform

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Background The emergency department (ED) triage system to classify and prioritize patients from high risk to less urgent continues to be a challenge. Objective This study, comprising 80,433 patients, aims to develop a machine learning algorithm prediction model of critical care outcomes for adult patients using information collected during ED triage and compare the performance with that of the baseline model using the Korean Triage and Acuity Scale (KTAS). Methods To predict the need for critical care, we used 13 predictors from triage information: age, gender, mode of ED arrival, the time interval between onset and ED arrival, reason of ED visit, chief complaints, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, oxygen saturation, and level of consciousness. The baseline model with KTAS was developed using logistic regression, and the machine learning model with 13 variables was generated using extreme gradient boosting (XGB) and deep neural network (DNN) algorithms. The discrimination was measured by the area under the receiver operating characteristic (AUROC) curve. The ability of calibration with Hosmer–Lemeshow test and reclassification with net reclassification index were evaluated. The calibration plot and partial dependence plot were used in the analysis. Results The AUROC of the model with the full set of variables (0.833-0.861) was better than that of the baseline model (0.796). The XGB model of AUROC 0.861 (95% CI 0.848-0.874) showed a higher discriminative performance than the DNN model of 0.833 (95% CI 0.819-0.848). The XGB and DNN models proved better reclassification than the baseline model with a positive net reclassification index. The XGB models were well-calibrated (Hosmer-Lemeshow test; P>.05); however, the DNN showed poor calibration power (Hosmer-Lemeshow test; P<.001). We further interpreted the nonlinear association between variables and critical care prediction. Conclusions Our study demonstrated that the performance of the XGB model using initial information at ED triage for predicting patients in need of critical care outperformed the conventional model with KTAS.

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Over-triage occurs when considering the patient's pain in Korean Triage and Acuity Scale (KTAS)

Cited by 29 publications

References 31 publications

Triage accuracy and causes of mistriage using the Korean Triage and Acuity Scale

Triage accuracy and causes of mistriage using the Korean Triage and Acuity Scale

Analysis of the Adequacy of Prehospital Emergency Medical Services Use of Patients Who Visited Emergency Departments in Korea from 2016 to 2018: Data from the National Emergency Department Information System

Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis

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