Influence of electronic triage decision-support on hospital admission, left without being seen and time to physician initial assessment in the emergency department

McLeod, Shelley; Grewal, Keerat; Thompson, Courtney; Thabane, Lehana; Borgundvaag, Bjug; Ovens, Howard; Scott, Steve; Ahmed, Tamer; Mittmann, Nicole; Worster, Andrew; Agoritsas, Thomas; Guyatt, Gordon

doi:10.1007/s43678-020-00043-1

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, Kovacs and Campbell [ 39 ] showed a 3% increased probability that a triage nurse assigned higher triage acuity scores compared to triage paramedics in non-crowded ED conditions and a 10% higher probability of assigning a more acute triage score in crowded ED conditions. Moreover, recent work has shown that eCTAS systemically reclassifies patients from higher to lower acuity scores [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Development of a machine learning-based acuity score prediction model for virtual care settings

Hall,

Galaev,

Gavrilov

et al. 2023

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Objective Healthcare is increasingly digitized, yet remote and automated machine learning (ML) triage prediction systems for virtual urgent care use remain limited. The Canadian Triage and Acuity Scale (CTAS) is the gold standard triage tool for in-person care in Canada. The current work describes the development of a ML-based acuity score modelled after the CTAS system. Methods The ML-based acuity score model was developed using 2,460,109 de-identified patient-level encounter records from three large healthcare organizations (Ontario, Canada). Data included presenting complaint, clinical modifiers, age, sex, and self-reported pain. 2,041,987 records were high acuity (CTAS 1–3) and 416,870 records were low acuity (CTAS 4–5). Five models were trained: decision tree, k-nearest neighbors, random forest, gradient boosting regressor, and neural net. The outcome variable of interest was the acuity score predicted by the ML system compared to the CTAS score assigned by the triage nurse. Results Gradient boosting regressor demonstrated the greatest prediction accuracy. This final model was tuned toward up triaging to minimize patient risk if adopted into the clinical context. The algorithm predicted the same score in 47.4% of cases, and the same or more acute score in 95.0% of cases. Conclusions The ML algorithm shows reasonable predictive accuracy and high predictive safety and was developed using the largest dataset of its kind to date. Future work will involve conducting a pilot study to validate and prospectively assess reliability of the ML algorithm to assign acuity scores remotely.

show abstract

Section: Discussionmentioning

confidence: 99%

Development of a machine learning-based acuity score prediction model for virtual care settings

Hall,

Galaev,

Gavrilov

et al. 2023

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…In another eCTAS-related study, the use of eCTAS showed little effect on hospital admission, rate of LWBS, and time to physician initial assessment compared to before its usage. 27 Recently, studies using machine learning are also actively progressing. Levin, et al 28 developed a machine-learning-based triage tool, which showed similar or improved results to ESI in identifying patients with critical outcomes.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Simple Age-Adjusted Objectified Korean Triage and Acuity Scale for Adult Patients Visiting the Emergency Department

Kim

Min³

et al. 2022

Yonsei Med J

View full text Add to dashboard Cite

Purpose The study aimed to develop an objectified Korean Triage and Acuity Scale (OTAS) that can objectively and quickly classify severity, as well as a simple age-adjusted OTAS (S-OTAS) that reflects age and evaluate its usefulness. Materials and Methods A retrospective analysis was performed of all adult patients who had visited the emergency department at three teaching hospitals. Sex, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, O 2 saturation, and consciousness level were collected from medical records. The OTAS was developed with objective criterion and minimal OTAS level, and S-OTAS was developed by adding the age variable. For usefulness evaluation, the 30-day mortality, the rates of computed tomography scan and emergency procedures were compared between Korean Triage and Acuity Scale (KTAS) and OTAS. Results A total of 44402 patients were analyzed. For 30-day mortality, S-OTAS showed a higher area under the curve (AUC) compared to KTAS (0.751 vs. 0.812 for KTAS and S-OTAS, respectively, p <0.001). Regarding the rates of emergency procedures, AUC was significantly higher in S-OTAS, compared to KTAS (0.807 vs. 0.830, for KTAS and S-OTAS, respectively, p =0.013). Conclusion S-OTAS showed comparative usefulness for adult patients visiting the emergency department as a triage tool compared to KTAS.

show abstract

Validity of the Korean triage and acuity scale in older patients compared to the adult group

Chung

Namgung

Lee

et al. 2023

Experimental Gerontology

View full text Add to dashboard Cite

Influence of electronic triage decision-support on hospital admission, left without being seen and time to physician initial assessment in the emergency department

Cited by 3 publications

References 8 publications

Development of a machine learning-based acuity score prediction model for virtual care settings

Development of a machine learning-based acuity score prediction model for virtual care settings

Development and Validation of Simple Age-Adjusted Objectified Korean Triage and Acuity Scale for Adult Patients Visiting the Emergency Department

Validity of the Korean triage and acuity scale in older patients compared to the adult group

Contact Info

Product

Resources

About