Oulousian Emily, Chung Seok Hoon, Ganni Elie, Razaghizad Amir, Zhang Guang, Avram Robert, Sharma Abhinav
DREAM-CV Lab, McGill University Health Centre, McGill University, Montreal, QC, Canada.
Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, McGill University, Montreal, QC, Canada.
JMIR Res Protoc. 2023 Jan 31;12:e41209. doi: 10.2196/41209.
The COVID-19 pandemic has disrupted the health care system, limiting health care resources such as the availability of health care professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools have become an important asset in increasing the efficiency of patient care delivery. Digital tools can help support health care institutions by tracking transmission of the virus, aiding in the screening process, and providing telemedicine support. However, digital health tools face challenges associated with barriers to accessibility, efficiency, and privacy-related ethical issues.
This paper describes the study design of an open-label, noninterventional, crossover, randomized controlled trial aimed at assessing whether interactive voice response systems can screen for SARS-CoV-2 in patients as accurately as standard screening done by people. The study aims to assess the concordance and interrater reliability of symptom screening done by Amazon Alexa compared to manual screening done by research coordinators. The perceived level of comfort of patients when interacting with voice response systems and their personal experience will also be evaluated.
A total of 52 patients visiting the heart failure clinic at the Royal Victoria Hospital of the McGill University Health Center, in Montreal, Quebec, will be recruited. Patients will be randomly assigned to first be screened for symptoms of SARS-CoV-2 either digitally, by Amazon Alexa, or manually, by the research coordinator. Participants will subsequently be crossed over and screened either digitally or manually. The clinical setup includes an Amazon Echo Show, a tablet, and an uninterrupted power supply mounted on a mobile cart. The primary end point will be the interrater reliability on the accuracy of randomized screening data performed by Amazon Alexa versus research coordinators. The secondary end point will be the perceived level of comfort and app engagement of patients as assessed using 5-point Likert scales and binary mode responses.
Data collection started in May 2021 and is expected to be completed in fall 2022. Data analysis is expected to be completed in early 2023.
The use of voice-based assistants could improve the provision of health services and reduce the burden on health care personnel. Demonstrating a high interrater reliability between Amazon Alexa and health care coordinators may serve future digital tools to streamline the screening and delivery of care in the context of other conditions and clinical settings. The COVID-19 pandemic occurs during the first digital era using digital tools such as Amazon Alexa for disease screening, and it represents an opportunity to implement such technology in health care institutions in the long term.
ClinicalTrials.gov NCT04508972; https://clinicaltrials.gov/ct2/show/NCT04508972.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41209.
新冠疫情扰乱了医疗系统，限制了医疗资源，如医疗专业人员的可及性、患者监测、接触者追踪和持续监测。由于这一巨大负担，数字工具已成为提高患者护理效率的重要资产。数字工具可通过追踪病毒传播、协助筛查过程以及提供远程医疗支持来帮助支持医疗机构。然而，数字健康工具面临与可及性障碍、效率以及隐私相关伦理问题有关的挑战。
本文描述了一项开放标签、非干预、交叉、随机对照试验的研究设计，旨在评估交互式语音应答系统能否像人工标准筛查一样准确地对患者进行新冠病毒筛查。该研究旨在评估亚马逊Alexa进行的症状筛查与研究协调员进行的人工筛查之间的一致性和评分者间信度。还将评估患者与语音应答系统交互时的舒适度感知水平及其个人体验。
将招募总共52名前往魁北克省蒙特利尔市麦吉尔大学健康中心皇家维多利亚医院心力衰竭诊所就诊的患者。患者将被随机分配，首先通过亚马逊Alexa进行数字方式或由研究协调员进行人工方式筛查新冠病毒症状。参与者随后将进行交叉，接受数字或人工筛查。临床设置包括一台亚马逊Echo Show、一台平板电脑以及安装在移动推车上的不间断电源。主要终点将是亚马逊Alexa与研究协调员进行的随机筛查数据准确性方面的评分者间信度。次要终点将是使用5分李克特量表和二元模式反应评估的患者舒适度感知水平和应用参与度。
数据收集于2021年5月开始，预计2022年秋季完成。数据分析预计2023年初完成。
使用基于语音的助手可改善医疗服务的提供并减轻医护人员的负担。证明亚马逊Alexa与医疗协调员之间具有较高的评分者间信度，可能有助于未来的数字工具在其他病症和临床环境中简化筛查和护理提供流程。新冠疫情发生在使用亚马逊Alexa等数字工具进行疾病筛查的首个数字时代，这代表着长期在医疗机构中应用此类技术的一个机会。
ClinicalTrials.gov NCT04508972；https://clinicaltrials.gov/ct2/show/NCT04508972。
国际注册报告识别码（IRRID）：DERR1-10.2196/41209。
