基于大型语言模型对患者在篮子中的信息的响应
William R Small1, Batia Wiesenfeld2, Beatrix Brandfield-Harvey1
1NYU Grossman School of Medicine, New York, New York.
JAMA network open
|July 16, 2024
概括
患者信息的生成人工智能 (GenAI) 草案被初级保健医生 (PCP) 评为比人类反应更有用和更富同情心. 然而,GenAI草稿的可读性较低,这对健康状况有限或英语素养有限的患者构成了挑战.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床交流 临床沟通
背景情况:
- 虚拟患者与医生的沟通激增,影响了初级保健医生 (PCP) 的福祉.
- 生成型人工智能 (GenAI) 具有减轻医疗保健专业人员 (HCP) 工作量和提高沟通质量的潜力.
研究的目的:
- 评估PCP对GenAI起草的患者信息的看法.
- 分析与公平性和同理心相关的GenAI草案的语言特征.
- 为了比较GenAI草稿与初级保健机构中人类生成的反应.
主要方法:
- 纽约大学兰贡健康的质量改进横截面研究.
- PCP对GenAI草稿和医疗保健人员生成的响应进行了评级,对信息内容,通信质量和可用性进行了评级.
- 计算语言学评估了同理心,个性化和专业性的差异.
主要成果:
- GenAI的草稿获得了有利的评分,与医疗保健人员在信息内容和可用性方面的回应相当.
- 对于沟通风格和感知到的同情心,GenAI的反应被评为更高.
- 与HCP回复相比,GenAI草稿表现出更主观和积极的语言,但可读性较低.
结论:
- 通过有效和同情地传递信息,GenAI聊天机器人有望改善患者-医疗保健人员的沟通.
- 对GenAI草稿的可读性是一个问题,可能会限制健康状况不佳或英语素养较低的患者的可访问性.
- 需要进一步的研究来优化GenAI以实现公平和可访问的临床沟通.
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