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A Comparison Between Computer-Assisted Self-Triage by Patients and Triage Performed by Nurses in the Emergency Department

RUH-ST-Comparing Self-Triage and Triage

Royal University Hospital (ED)

Background and objective: Emergency departments (EDs) often find the number of arriving patients exceeding their capacity and find it difficult to triage them in a timely manner. The potential risk to the safety of patients awaiting assessment by a triage professional has led some hospitals to consider implementing patient self-triage, such as using kiosks. Published studies about patient self-triage are scarce and information about patients’ ability to accurately assess the acuity of their condition or predict their need to be hospitalized is limited. In this study, we aimed to compare computer-assisted patient self-triage scores versus the scores assigned by the dedicated ED triage nurse (TN).

Methods: This pilot study enrolled patients presenting to a tertiary care hospital ED without ambulance transport. They were asked a short series of simple questions based on an algorithm, which then generated a triage score. Patients were asked whether they were likely to be admitted to the hospital. Patients then entered the usual ED system of triage. The algorithm-generated triage score was then compared with the Canadian Triage and Acuity Scale (CTAS) score assigned by the TN. Whether the patients actually required hospital admission was determined by checking their medical records.

Results: Among the 492 patients enrolled, agreement of triage scores was observed in 27%. Acuity was overestimated by 65% of patients. Underestimation of acuity occurred in 8%. Among patients predicting hospitalization, 17% were admitted, but the odds ratio (OR) for admission was 3.4. Half of the patients with   cardiorespiratory complaints were correct in predicting the need for hospitalization.

Conclusion: The use of a short questionnaire by patients to self-triage showed limited accuracy, but sensitivity was high for some serious medical conditions. The prediction of hospitalization was more accurate with regard to cardiorespiratory complaints.

Authors: Sachin Trivedi, Jessica Littmann, James Stempien, Puneet Kapur, Rhonda Bryce, Martin Betz

Sachin Trivedi -

Preliminary data gathering/ baseline

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