Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Appl Clin Inform. 2021 Oct;12(5):1021-1028. doi: 10.1055/s-0041-1736627. Epub 2021 Nov 3.
We describe the design, implementation, and validation of an online, publicly available tool to algorithmically triage patients experiencing severe acute respiratory syndrome coronavirus (SARS-CoV-2)-like symptoms.
We conducted a chart review of patients who completed the triage tool and subsequently contacted our institution's phone triage hotline to assess tool- and clinician-assigned triage codes, patient demographics, SARS-CoV-2 (COVID-19) test data, and health care utilization in the 30 days post-encounter. We calculated the percentage of concordance between tool- and clinician-assigned triage categories, down-triage (clinician assigning a less severe category than the triage tool), and up-triage (clinician assigning a more severe category than the triage tool) instances.
From May 4, 2020 through January 31, 2021, the triage tool was completed 30,321 times by 20,930 unique patients. Of those 30,321 triage tool completions, 51.7% were assessed by the triage tool to be asymptomatic, 15.6% low severity, 21.7% moderate severity, and 11.0% high severity. The concordance rate, where the triage tool and clinician assigned the same clinical severity, was 29.2%. The down-triage rate was 70.1%. Only six patients were up-triaged by the clinician. 72.1% received a COVID-19 test administered by our health care system within 14 days of their encounter, with a positivity rate of 14.7%.
The design, pilot, and validation analysis in this study show that this COVID-19 triage tool can safely triage patients when compared with clinician triage personnel. This work may signal opportunities for automated triage of patients for conditions beyond COVID-19 to improve patient experience by enabling self-service, on-demand, 24/7 triage access.
我们描述了一种在线、公开的工具的设计、实施和验证,该工具可用于对出现严重急性呼吸综合征冠状病毒(SARS-CoV-2)样症状的患者进行算法分诊。
我们对完成分诊工具并随后联系我们机构电话分诊热线的患者进行了图表回顾,以评估工具和临床医生分配的分诊代码、患者人口统计学、SARS-CoV-2(COVID-19)检测数据以及接触后 30 天内的医疗保健利用情况。我们计算了工具和临床医生分配的分诊类别、下分诊(临床医生分配的类别比分诊工具轻)和上分诊(临床医生分配的类别比分诊工具重)之间的一致性百分比。
从 2020 年 5 月 4 日至 2021 年 1 月 31 日,该分诊工具由 20930 名不同的患者完成了 30321 次。在这 30321 次分诊工具完成中,51.7%被分诊工具评估为无症状,15.6%为低严重程度,21.7%为中度严重程度,11.0%为高严重程度。工具和临床医生分配相同临床严重程度的一致性率为 29.2%。下分诊率为 70.1%。只有 6 名患者被临床医生上调。72.1%的患者在接触后 14 天内接受了我们医疗系统管理的 COVID-19 检测,阳性率为 14.7%。
本研究中的设计、试点和验证分析表明,与临床分诊人员相比,该 COVID-19 分诊工具可以安全地对患者进行分诊。这项工作可能为超越 COVID-19 的条件下的患者自动分诊提供机会,通过启用自助服务、随需应变、24/7 分诊访问来改善患者体验。