在线医疗调查中识别和分析机器人生成的响应:方法论研究
Emily Hamovitch1, Kaileah McKellar1, Walter P Wodchis1,2
1Institute of Health Policy, Management and Evaluation, University of Toronto, 155 College Street, Toronto, ON, M5T 3M6, Canada, 1 (416) 978-4326.
JMIR medical informatics
|March 11, 2026
概括
在线健康调查容易受到机器人响应的影响,从而损害数据完整性. 这项研究制定了检测机器人的标准,发现了响应的显著差异和逆转的健康指标关系,强调了数字健康研究中对机器人检测的需求.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 数字健康数字健康
- 数据完整性 数据完整性
背景情况:
- 在医疗保健研究中,患者报告的结果越来越依赖在线调查.
- 人们越来越担心欺诈性的机器人响应威胁到数据完整性和有效性.
- 可能导致扭曲的统计分析和错误的健康政策决策.
研究的目的:
- 制定标准来识别在线医疗保健调查中机器人生成的响应.
- 检查机器人响应对数据质量和调查结果的影响.
- 在可能的人类和可疑机器人受访者之间比较调查数据.
主要方法:
- 进行了在线调查 (2023年7月至10月) 关于医疗保健使用情况,患者体验和结果.
- 开发了一个三级分类系统,使用"红旗" (例如,重复响应,时间不一致,位置不一致) 来检测机器人.
- 使用奇平方和斯皮尔曼相关性测试进行对差异和关系的定量分析.
主要成果:
- 在1154个回复中,58%被归类为疑似机器人生成的.
- 重复的开放式响应是最常见的机器人指标 (44%).
- 在机器人和人类之间观察到显著的差异;机器人倾向于中等的利克特级别反应,而人类选择极端. 在机器人数据中,健康指标之间的预期关系被反转了.
结论:
- 实施机器人预防和检测对于保持在线健康调查数据完整性至关重要.
- 机器人无法检测到机器人可能会扭曲研究结果,特别是在健康公平研究中.
- 有效的机器人检测策略包括开放文本分析,时间评估和地理验证; 持续的进步是必要的.
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