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

Moon, Sun-Hee; Shim, Jae Lan; Park, Keun Sook; Park, Chon Suk

doi:10.1371/journal.pone.0216972

Cited by 40 publications

(59 citation statements)

References 22 publications

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“…This system requires immediate treatment for patients at level 1, where immediate resuscitation is required. Level 2 patients (emergency) are required to receive treatment within 15 minutes, level 3 patients (urgent) within 30 minutes, level 4 patients (less urgent) within one hour, and level 5 patients (non-urgent) within two hours [22].…”

Section: Participantsmentioning

confidence: 99%

Factors influencing the use of health services by trauma patients according to insurance type and injury severity score in South Korea: Based on Andersen’s behavioral model

Kim

2020

PLoS ONE

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This study aims to understand the difference in trauma patients' use of health services in Korea according to insurance type and the Injury Severity Score. Andersen's behavioral model of health service use is employed to identify the factors influencing their use. Claims data from January 1 to December 31, 2016 were extracted from both the Health Insurance Review and Assessment Service and the automobile insurance screening center for all the medical treatments identified with the Korean Triage and Acuity Scale and Injury Severity Score. Using the Health Insurance Review and Assessment Service's remote statistical analysis system, hierarchical regression and negative binomial analyses were conducted to determine the effect of predisposing, enabling, and need factors on health service use. The results demonstrate that the use of Korean health services is relatively equitable since medical expenses for trauma patients are greatly influenced by need factors. However, the length of time trauma patients stay in the hospital appears to differ according to insurance type. This study suggests that healthcare policies need to increase coverage benefits and improve medical billing for patients with severe trauma, as well as develop a more robust screening system for patients with mild to moderate impairments.

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Section: Participantsmentioning

confidence: 99%

Factors influencing the use of health services by trauma patients according to insurance type and injury severity score in South Korea: Based on Andersen’s behavioral model

Kim

2020

PLoS ONE

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“…Furthermore, even though the AUROC of the models may be acceptable in some instances, it cannot be denied that further improvements would be desirable. However, it must be noted that even between experienced triage nurses, the interrater weighted-kappa value was 0.772 [26], and 0.83 between triage nurses and experts in a retrospective review [27]. Although there is room for further improvement, even at its current state, the prediction model may be of assistance to triage personnel as an adjunct tool because expertise in triage instruments requires considerable training and may be limited in resource-constrained settings.…”

Section: Discussionmentioning

confidence: 99%

“…This demand was clearly shown in a survey of triage nurses, where the most requested feature for a new tool being built was an automatic severity grade calculator for the emergency severity index, which is a widely used triage instrument [28]. Due to the inherent complexity and uncertainty involved with the triage of patients, misclassification, over-triage, and under-triage are always possible [27], and a rule-based decision support system for triage has been shown to reduce classification errors [29]. A support tool based on machine learning and NLP may also reduce triage errors and may be more robust to out-of-vocabulary terms than a rule-based system; this would be worth exploring in future studies.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients

Choi

Hong

et al. 2019

Healthc Inform Res

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ObjectivesTriage is a process to accurately assess and classify symptoms to identify and provide rapid treatment to patients. The Korean Triage and Acuity Scale (KTAS) is used as a triage instrument in all emergency centers. The aim of this study was to train and compare machine learning models to predict KTAS levels.MethodsThis was a cross-sectional study using data from a single emergency department of a tertiary university hospital. Information collected during triage was used in the analysis. Logistic regression, random forest, and XGBoost were used to predict the KTAS level.ResultsThe models with the highest area under the receiver operating characteristic curve (AUROC) were the random forest and XGBoost models trained on the entire dataset (AUROC = 0.922, 95% confidence interval 0.917–0.925 and AUROC = 0.922, 95% confidence interval 0.918–0.925, respectively). The AUROC of the models trained on the clinical data was higher than that of models trained on text data only, but the models trained on all variables had the highest AUROC among similar machine learning models.ConclusionsMachine learning can robustly predict the KTAS level at triage, which may have many possibilities for use, and the addition of text data improves the predictive performance compared to that achieved by using structured data alone.

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“…The type of patient is classified as either adult or pediatric, and they are separated to get treatments in different areas. Their acuity is categorized by the triage method, and their clinical conditions are evaluated according to a symptom-oriented classification tool [32]. After triage, the patient is assigned a bed with priority given to cases of high acuity level; thus, some patients may not be assigned beds.…”

Section: Emergency Department Modelmentioning

confidence: 99%

Improving Emergency Department Efficiency by Patient Scheduling Using Deep Reinforcement Learning

Lee¹,

Lee²

2020

Healthcare

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Emergency departments (ED) in hospitals usually suffer from crowdedness and long waiting times for treatment. The complexity of the patient’s path flows and their controls come from the patient’s diverse acute level, personalized treatment process, and interconnected medical staff and resources. One of the factors, which has been controlled, is the dynamic situation change such as the patient’s composition and resources’ availability. The patient’s scheduling is thus complicated in consideration of various factors to achieve ED efficiency. To address this issue, a deep reinforcement learning (RL) is designed and applied in an ED patients’ scheduling process. Before applying the deep RL, the mathematical model and the Markov decision process (MDP) for the ED is presented and formulated. Then, the algorithm of the RL based on deep Q -networks (DQN) is designed to determine the optimal policy for scheduling patients. To evaluate the performance of the deep RL, it is compared with the dispatching rules presented in the study. The deep RL is shown to outperform the dispatching rules in terms of minimizing the weighted waiting time of the patients and the penalty of emergent patients in the suggested scenarios. This study demonstrates the successful implementation of the deep RL for ED applications, particularly in assisting decision-makers under the dynamic environment of an ED.

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

Cited by 40 publications

References 22 publications

Factors influencing the use of health services by trauma patients according to insurance type and injury severity score in South Korea: Based on Andersen’s behavioral model

Factors influencing the use of health services by trauma patients according to insurance type and injury severity score in South Korea: Based on Andersen’s behavioral model

Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients

Improving Emergency Department Efficiency by Patient Scheduling Using Deep Reinforcement Learning

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