Trivedi Sachin, Littmann Jessica, Stempien James, Kapur Puneet, Bryce Rhonda, Betz Martin
Emergency Medicine, Royal University Hospital, University of Saskatchewan, Saskatoon, CAN.
Orthopedics, University of Manitoba, Winnipeg, CAN.
Cureus. 2021 Mar 19;13(3):e14002. doi: 10.7759/cureus.14002.
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.
背景与目的 急诊科常常面临就诊患者数量超出其接待能力的情况,且难以对患者进行及时分诊。等待分诊专业人员评估的患者的安全存在潜在风险,这促使一些医院考虑实施患者自我分诊,例如使用信息亭。关于患者自我分诊的已发表研究很少,而且关于患者准确评估自身病情严重程度或预测住院需求能力的信息有限。在本研究中,我们旨在比较计算机辅助的患者自我分诊分数与急诊科专职分诊护士(TN)给出的分数。
方法 这项前瞻性研究纳入了到三级医疗医院急诊科就诊且未通过救护车转运的患者。他们根据一种算法被问到一系列简短的简单问题,然后生成一个分诊分数。患者被询问是否可能住院。然后患者进入常规的急诊科分诊系统。接着将算法生成的分诊分数与TN给出的加拿大分诊与 acuity 量表(CTAS)分数进行比较。通过查看患者病历确定患者是否实际需要住院。
结果 在纳入的492例患者中,分诊分数的一致性为27%。65%的患者高估了病情严重程度。8%的患者低估了病情严重程度。在预测住院的患者中,17%被收治,但住院的比值比(OR)为3.4。一半有心肺症状的患者在预测住院需求方面是正确的。
结论 患者使用简短问卷进行自我分诊的准确性有限,但对一些严重病症的敏感性较高。对于心肺症状,住院预测更为准确。