Morse Keith E, Ostberg Nicolai P, Jones Veena G, Chan Albert S
Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, United States.
Center for Biomedical Informatics Research, Stanford University School of Medicine, Palo Alto, CA, United States.
J Med Internet Res. 2020 Nov 30;22(11):e20549. doi: 10.2196/20549.
Pressure on the US health care system has been increasing due to a combination of aging populations, rising health care expenditures, and most recently, the COVID-19 pandemic. Responses to this pressure are hindered in part by reliance on a limited supply of highly trained health care professionals, creating a need for scalable technological solutions. Digital symptom checkers are artificial intelligence-supported software tools that use a conversational "chatbot" format to support rapid diagnosis and consistent triage. The COVID-19 pandemic has brought new attention to these tools due to the need to avoid face-to-face contact and preserve urgent care capacity. However, evidence-based deployment of these chatbots requires an understanding of user demographics and associated triage recommendations generated by a large general population.
In this study, we evaluate the user demographics and levels of triage acuity provided by a symptom checker chatbot deployed in partnership with a large integrated health system in the United States.
This population-based descriptive study included all web-based symptom assessments completed on the website and patient portal of the Sutter Health system (24 hospitals in Northern California) from April 24, 2019, to February 1, 2020. User demographics were compared to relevant US Census population data.
A total of 26,646 symptom assessments were completed during the study period. Most assessments (17,816/26,646, 66.9%) were completed by female users. The mean user age was 34.3 years (SD 14.4 years), compared to a median age of 37.3 years of the general population. The most common initial symptom was abdominal pain (2060/26,646, 7.7%). A substantial number of assessments (12,357/26,646, 46.4%) were completed outside of typical physician office hours. Most users were advised to seek medical care on the same day (7299/26,646, 27.4%) or within 2-3 days (6301/26,646, 23.6%). Over a quarter of the assessments indicated a high degree of urgency (7723/26,646, 29.0%).
Users of the symptom checker chatbot were broadly representative of our patient population, although they skewed toward younger and female users. The triage recommendations were comparable to those of nurse-staffed telephone triage lines. Although the emergence of COVID-19 has increased the interest in remote medical assessment tools, it is important to take an evidence-based approach to their deployment.
由于人口老龄化、医疗保健支出不断增加，以及最近的新冠疫情，美国医疗保健系统面临的压力一直在增大。对这一压力的应对措施在一定程度上受到依赖数量有限的训练有素的医疗保健专业人员的阻碍，因此需要可扩展的技术解决方案。数字症状检查器是人工智能支持的软件工具，采用对话式“聊天机器人”格式来支持快速诊断和一致的分诊。由于需要避免面对面接触并保留紧急护理能力，新冠疫情使这些工具受到了新的关注。然而，基于证据部署这些聊天机器人需要了解用户人口统计学特征以及广大普通人群生成的相关分诊建议。
在本研究中，我们评估了与美国一家大型综合医疗系统合作部署的症状检查器聊天机器人提供的用户人口统计学特征和分诊敏锐度水平。
这项基于人群的描述性研究包括2019年4月24日至2020年2月1日在萨特健康系统（加利福尼亚州北部的24家医院）的网站和患者门户网站上完成的所有基于网络的症状评估。将用户人口统计学特征与美国人口普查相关人口数据进行比较。
在研究期间共完成了26646次症状评估。大多数评估（17816/26646，66.9%）由女性用户完成。用户平均年龄为34.3岁（标准差14.4岁），而普通人群的年龄中位数为37.3岁。最常见的初始症状是腹痛（2060/26646，7.7%）。相当数量的评估（12357/26646，46.4%）是在非典型医生办公时间完成的。大多数用户被建议在当天（7299/26646，27.4%）或2至3天内（6301/26646，23.6%）寻求医疗护理。超过四分之一的评估表明紧急程度很高（7723/26646，29.0%）。
症状检查器聊天机器人的用户在很大程度上代表了我们的患者群体，尽管他们偏向于年轻和女性用户。分诊建议与护士值守的电话分诊热线相当。尽管新冠疫情的出现增加了对远程医疗评估工具的兴趣，但以基于证据的方法来部署这些工具很重要。
