患者沟通风格对代理人工智能产生的临床建议在电子医学中的影响
Mahmud Omar1, Alon Gorenshtein2, Reem Agbareia3
1The Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Health System, NY, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Mount Sinai Health System, NY, USA; Maccabi Health Services, Tel-Aviv, Israel.
The American journal of medicine
|January 22, 2026
概括
患者的语气显著影响电子医疗中的AI决策,影响分拣和病假批准. 这突显了人工智能 (AI) 临床工具中的潜在偏见.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 临床决策支持 临床决策支持
背景情况:
- 电子医疗平台越来越普遍,促使人们探索大型语言模型 (LLM) 进行管理任务,例如消息分类.
- 患者沟通风格对人工智能驱动的临床和行政决策的影响在很大程度上仍未得到研究.
研究的目的:
- 调查患者的语气是否能改变人工智能在电子医学中的临床或行政决策.
- 评估各种沟通风格对LLM产出的影响,用于分拣和病假情景.
主要方法:
- 开发了1000个临床医生验证的简报 (500个临床,500个病假),以八种沟通方式呈现.
- 利用五种代理大型语言模型 (LLM) 来生成用于分拣紧急情况和病假决定的结构化输出.
- 分析了与中性音调的差异,使用基平方和t测试与FDR校正;通过40个真实患者的电子邮件进行验证.
主要成果:
- 患者语调显然在12万次运行中转移了AI输出,紧急/威胁语调增加了当日护理请求 (14%至37-63%).
- 药物处方建议在某些音调 (5%至7-9%) 中略有增加,而情感音调则提高了同理心反应 (62%至70-86%).
- 威胁的声音减少了病假的批准 (58%至50%),并给予了假期,现实世界的消息证实了声音的影响.
结论:
- 代理的LLM将患者的声音作为临床输入,影响分拣,处方和病假决策,即使有相同的症状.
- 音调敏感的人工智能输出可能引入隐藏的偏见,影响医疗保健资源分配,并存在电子医疗滥用风险.
- 研究结果强调需要仔细开发和验证人工智能,以减轻数字健康沟通中的偏见.
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