Pre-triage wait times for non-ambulance arrivals in the emergency department: A retrospective video audit

Sedgman, Rebecca; Aldridge, Emogene; Miller, Joseph; Fleming, Daniel; Buntine, Paul

doi:10.1016/j.auec.2021.11.002

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…However, for patients arriving at the emergency department (ED), registration is normally completed by triage nurses who communicate with patients and then interact with computers for data entry. Such nurse-led registration often takes time and slows down the triage process [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…When a patient presenting at the ED is seen, others have to wait [ 2 ]. During peak hours, pretriage waiting tends to be prolonged and unpredictable [ 2 - 4 ], representing one of the main factors contributing to ED overcrowding [ 5 , 6 ]. An extended waiting time in the ED has been associated with increased morbidity and mortality rates [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Use of Mobile Technologies to Streamline Pretriage Patient Flow in the Emergency Department: Observational Usability Study

Wang,

Yu,

et al. 2024

JMIR Mhealth Uhealth

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Background In emergency departments (EDs), triage nurses are under tremendous daily pressure to rapidly assess the acuity level of patients and log the collected information into computers. With self-service technologies, patients could complete data entry on their own, allowing nurses to focus on higher-order tasks. Kiosks are a popular working example of such self-service technologies; however, placing a sufficient number of unwieldy and fixed machines demands a spatial change in the greeting area and affects pretriage flow. Mobile technologies could offer a solution to these issues. Objective The aim of this study was to investigate the use of mobile technologies to improve pretriage flow in EDs. Methods The proposed stack of mobile technologies includes patient-carried smartphones and QR technology. The web address of the self-registration app is encoded into a QR code, which was posted directly outside the walk-in entrance to be seen by every ambulatory arrival. Registration is initiated immediately after patients or their proxies scan the code using their smartphones. Patients could complete data entry at any site on the way to the triage area. Upon completion, the result is saved locally on smartphones. At the triage area, the result is automatically decoded by a portable code reader and then loaded into the triage computer. This system was implemented in three busy metropolitan EDs in Shanghai, China. Both kiosks and smartphones were evaluated randomly while being used to direct pretriage patient flow. Data were collected during a 20-day period in each center. Timeliness and usability of medical students simulating ED arrivals were assessed with the After-Scenario Questionnaire. Usability was assessed by triage nurses with the Net Promoter Score (NPS). Observations made during system implementation were subject to qualitative thematic analysis. Results Overall, 5928 of 8575 patients performed self-registration on kiosks, and 7330 of 8532 patients checked in on their smartphones. Referring effort was significantly reduced (43.7% vs 8.8%; P<.001) and mean pretriage waiting times were significantly reduced (4.4, SD 1.7 vs 2.9, SD 1.0 minutes; P<.001) with the use of smartphones compared to kiosks. There was a significant difference in mean usability scores for “ease of task completion” (4.4, SD 1.5 vs 6.7, SD 0.7; P<.001), “satisfaction with completion time” (4.5, SD 1.4 vs 6.8, SD 0.6; P<.001), and “satisfaction with support” (4.9, SD 1.9 vs 6.6, SD 1.2; P<.001). Triage nurses provided a higher NPS after implementation of mobile self-registration compared to the use of kiosks (13.3% vs 93.3%; P<.001). A modified queueing model was identified and qualitative findings were grouped by sequential steps. Conclusions This study suggests patient-carried smartphones as a useful tool for ED self-registration. With increased usability and a tailored queueing model, the proposed system is expected to minimize pretriage waiting for patients in the ED.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Mobile Technologies to Streamline Pretriage Patient Flow in the Emergency Department: Observational Usability Study

Wang,

Yu,

et al. 2024

JMIR Mhealth Uhealth

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“…‘Urgency’ is determined according to the patient’s condition on arrival at the EDs. Internationally, five-tier triage scales have been shown to be a valid and reliable method for categorising people who are seeking assessment and treatment in hospital EDs 2 . Two of the most widely adopted scales are the Manchester Triage Score (MTS) and Canadian Triage & Acuity Score (CTAS).…”

Section: Introductionmentioning

confidence: 99%

“…Pre-triage waiting times for those patients presenting to the ED can be long during peak hours 2 . Patients presenting to the ED are often required to wait until a trained triage nurse is available to initiate the triage process.…”

Section: Introductionmentioning

confidence: 99%

The use of kiosks to improve triage efficiency in the emergency department

Joseph

Summerscales²,

Yogesan³

et al. 2023

npj Digit. Med.

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Triage is a system used to prioritise patients as they enter the emergency department (ED) based on their need for urgent care. In recent decades, EDs have becoming increasingly overcrowded, leading to longer pre-triage waiting times for patients. E-triage interventions like kiosks have been proposed as a solution to overcrowding. We conducted a literature review into the effectiveness of kiosks in improving triage efficiency. After rigorously searching five biomedical databases and screening candidate articles in Endnote, we identified nine papers pertaining to the introduction of kiosks in emergency departments. Six articles had positive findings—with E-triage interventions improving some aspect of the triage process—such as reducing pre-triage times. Conversely, only three articles reported negative findings, such as low uptake. Consequently, EDs should consider introducing kiosks to complement the current nurse-led triage process and thereby promote better patient outcomes.

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“…Such a triage system allows the hospital to quickly focus on collecting data, and data analysis, judgment, classification, and division [ 2 ]. Due to the lack of medical knowledge reserve of the patient population, the corresponding relationship between the discipline setting of large hospitals and clinical diseases is more complicated [ 3 ], and it is often impossible to quickly locate the outpatient department of symptomatic treatment, and there are more ineffective visits [ 4 ]. Medical staff, such as triage nurses, can help some patients, but due to the large population base of patients, problems such as triage inefficiency still need to be solved.…”

Section: Introductionmentioning

confidence: 99%

A Disease-Prediction Protocol Integrating Triage Priority and BERT-Based Transfer Learning for Intelligent Triage

et al. 2023

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Large hospitals can be complex, with numerous discipline and subspecialty settings. Patients may have limited medical knowledge, making it difficult for them to determine which department to visit. As a result, visits to the wrong departments and unnecessary appointments are common. To address this issue, modern hospitals require a remote system capable of performing intelligent triage, enabling patients to perform self-service triage. To address the challenges outlined above, this study presents an intelligent triage system based on transfer learning, capable of processing multilabel neurological medical texts. The system predicts a diagnosis and corresponding department based on the patient’s input. It utilizes the triage priority (TP) method to label diagnostic combinations found in medical records, converting a multilabel problem into a single-label one. The system considers disease severity and reduces the “class overlapping” of the dataset. The BERT model classifies the chief complaint text, predicting a primary diagnosis corresponding to the complaint. To address data imbalance, a composite loss function based on cost-sensitive learning is added to the BERT architecture. The study results indicate that the TP method achieves a classification accuracy of 87.47% on medical record text, outperforming other problem transformation methods. By incorporating the composite loss function, the system’s accuracy rate improves to 88.38% surpassing other loss functions. Compared to traditional methods, this system does not introduce significant complexity, yet substantially improves triage accuracy, reduces patient input confusion, and enhances hospital triage capabilities, ultimately improving the patient’s medical experience. The findings could provide a reference for intelligent triage development.

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Pre-triage wait times for non-ambulance arrivals in the emergency department: A retrospective video audit

Cited by 5 publications

References 14 publications

Use of Mobile Technologies to Streamline Pretriage Patient Flow in the Emergency Department: Observational Usability Study

Use of Mobile Technologies to Streamline Pretriage Patient Flow in the Emergency Department: Observational Usability Study

The use of kiosks to improve triage efficiency in the emergency department

A Disease-Prediction Protocol Integrating Triage Priority and BERT-Based Transfer Learning for Intelligent Triage

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